大数据生态系统 修仙之道 Hive Blog
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- 大数据生态系统 修仙之道 Hive Blog
- 🐘 Hive Technology 修仙之道 炼气化神 🐘
- 1. Hive 基本概述
- 2. Hive 安装部署
- 2.1 Hive Download Link
- 2.2 启动Hadoop集群
- 3. Hive 数据类型
- 4. DDL 数据定义
- 5. DML 数据操作
- 6. 查询
- 6.1 基本查询 (Select…From)
- 6.2 Where语句
- 6.3 分组
- 6.4 Join语句
- 6.5 排序
- 6.6 分桶及抽样查询
- 6.7 其他常用查询函数
- 7. 函数
- 7.3 自定义UDF函数
- 8. 压缩 & 存储
- 9. 企业级调优
- 🔒 尚未解锁 正在学习探索中… 尽情期待 Blog更新! 🔒
- 10. 修仙之道 技术架构迭代 登峰造极之势
- 💡如何对该开源文档进行贡献💡
- 💌感谢您的阅读 欢迎您的留言与建议💌
- License 开源协议
1. Hive 基本概述
1.1 🤔 什么是Hive 🤔
Hive 由FaceBook开源并用于解决海量结构化日志的数据统计分析.
Hive是基于Hadoop的一个数据仓库工具,可以将结构化的数据文件映射为一张数据表,并提供类SQL查询功能.
本质是: 将HQL转化成MapReduce程序.
1.Hive处理的数据存储在HDFS.
2.Hive分析数据底层的实现是MapReduce.
3.执行程序运行在Yarn上.
1.1.2 Hive 特性
Hive作为数据仓库软件,使用类SQL的HiveQL语言实现数据查询,所有Hive数据均存储在Hadoop分布式文件系统中,Hive具有以下特征.
使用HiveQL以类似SQL查询方式轻松访问数据,将HQL查询转换为MapReduce的任务在Hadoop集群上执行,完成ETL(提取 / 转换 / 加载 / 报表 / 数据分析)等数据仓库任务.
多种文件格式的元数据服务,包括TextFile / SequuenceFile / RCFile / ORCFile,其中默认格式为TextFile.
直接访问HDFS文件或其他数据存储系统(如: HBase)中的文件.
支持MapReduce / Teza / Spark 等多种计算引擎,开发者可根据不同的数据处理场景选择合适的计算引擎.
支持HPL/SQL程序语言,HPL/SQL是一种混合异构的语言,可以理解几乎任何现有的过程性SQL语言的语法和语义,有助于将传统数据仓库的业务逻辑迁移到Hadoop上,在Hadoop上实现ETL流程的有效方式.
可以通过HiveLLAP,Yarn进行秒级别的查询检索,LLAP结合了持久查询服务器和优化的内存缓存,使Hive能够立即启动查询,避免不必要的磁盘开销,提供较佳的查询检索效率.
1.1.3 Hive 应用场景
Hive构建在Hadoop文件系统之上,Hive不提供实时的查询和基于行级数据的更新操作,不适合需要低延时作用的应用,如联机事务处理相关应用.
| 类别 | 具体应用场景 |
| 数据挖掘 | 用户行为分析 / 兴趣分区 / 区域展示 |
| 非实时分析挖掘 | 日志分析 / 文本分析 |
| 数据汇总 | 用户点击量统计 / 流量统计 |
| 数据仓库 | 数据抽取 / 数据加载 / 数据转换 |
1.2 HIve 优缺点
优点
1.操作接口采用类SQL语法,提供快速开发的能力(简单容易上手).
2.避免了去写MapReduce,减少开发人员的学习成本.
3.Hive的执行延迟比较高,因此Hive常用于数据分析,对实时性要求不高的场合.
4.Hive优势在于处理大数据,对于处理小数据没有优势,因为Hive的执行延迟比较高.
5.Hive支持开发者自定义函数,开发者可以根据自己的需求来实现自己的函数.
缺点
1.Hive的HQL表达能力有限
(1) 迭代式算法无法表达.
(2) 数据挖掘方面不擅长.2.Hive的效率比较低
(1) Hive自动生成的MapReduce作业,通常情况下不够智能化.
(2) Hive调优比较困难,粒度较粗.
1.3 HIve 架构原理 🤔🤔🤔
Hive通过给开发者提供的一系列交互接口,接收到开发者的指令(SQL),使用自己的Driver,结合元数据(MetaStore),将这些指令翻译成MapReduce,提交到Hadoop中执行,最后,将执行返回的结果输出到开发者交互接口.
1.用户接口: Client
CLI (Hive Shell) / JDBC/ODBC (Java访问Hive) / WEBUI (浏览器访问Hive).
2.元数据: Metastore
元数据包括: 表名、表所属的数据库(默认是default)、表的拥有者,列/分区字段/表的类型(是否是外部表),表的数据所在目录等.
默认存储在自带的derby数据库中,推荐使用MySQL存储Metastore.
3.Hadoop
使用HDFS进行存储,使用MapReduce进行计算.
4.驱动器: Driver
(1) 解析器(SQL Parser): 将SQL字符串转换成抽象语法树AST,这一步一般都用第三方工具库完成,比如antlr,对AST进行语法分析,比如表是否存在、字段是否存在、SQL语义是否有误.
(2) 编译器(Physical Plan): 将AST编译生成逻辑执行计划.
(3) 优化器(Query Optimizer): 对逻辑执行计划进行优化.
(4) 执行器(Execution): 把逻辑执行计划转换成可以运行的物理计划,对于Hive来说,就是MR & Spark.
1.4 HIve & 数据库比较
由于Hive采用了类似SQL的查询语言HQL(Hive Query Language),因此很容易将Hive理解为数据库.
其实从结构上来看,Hive和数据库除了拥有类似的查询语言,再无类似之处,数据库可以用在Online的应用中,但是Hive是为数据仓库而设计的,清楚这一点,有助于从应用角度理解Hive的特性.
1.4.1 查询语言
由于SQL被广泛的应用在数据仓库中,因此,专门针对Hive的特性设计了类SQL的查询语言HQL,熟悉SQL开发的开发者可以很方便的使用Hive进行开发.
1.4.2 数据存储位置
Hive是建立在Hadoop之上的,所有Hive的数据都是存储在HDFS中的,而数据库则可以将数据保存在块设备或者本地文件系统中.
1.4.3 数据更新
由于Hive是针对数据仓库应用设计的,而数据仓库的内容是读多写少的,因此Hive中不建议对数据的改写,所有的数据都是在加载的时候确定好的.
而数据库中的数据通常是需要经常进行修改的,因此可以使用
INSERTINTO...VALUES添加数据,使用UPDATE...SET修改数据.
1.4.4 索引
Hive在加载数据的过程中不会对数据进行任何处理,甚至不会对数据进行扫描,因此也没有对数据中的某些Key建立索引.
Hive要访问数据中满足条件的特定值时,需要暴力扫描整个数据,因此访问延迟较高.
由于MapReduce的引入,Hive可以并行访问数据,因此即使没有索引,对于大数据量的访问,Hive仍然可以体现出优势.
数据库中,通常会针对一个或者几个列建立索引,因此对于少量的特定条件的数据的访问,数据库可以有很高的效率,较低的延迟,由于数据的访问延迟较高,决定了Hive不适合在线数据查询.
1.4.5 执行
Hive中大多数查询的执行是通过Hadoop提供的MapReduce来实现的,而数据库通常有自己的执行引擎.
1.4.6 执行延迟
Hive在查询数据的时候,由于没有索引,需要扫描整个数据表,因此延迟较高,另外一个导致Hive执行延迟高的因素是MapReduce框架.
由于MapReduce本身具有较高的延迟,因此在利用MapReduce执行Hive查询时,也会有较高的延迟,相对的数据库的执行延迟较低,当然,这个低是有条件的,即数据规模较小,当数据规模大到超过数据库的处理能力的时候,Hive的并行计算显然能体现出优势.
1.4.7 可扩展性
由于Hive是建立在Hadoop之上的,因此Hive的可扩展性是和Hadoop的可扩展性是一致的(世界上最大的Hadoop集群在Yahoo!,2009年的规模在4000台节点左右),而数据库由于ACID语义的严格限制,扩展行非常有限,目前最先进的并行数据库Oracle在理论上的扩展能力也只有100台左右.
1.4.8 数据规模
由于Hive建立在集群上并可以利用MapReduce并行计算,因此可以支持很大规模的数据,对应的数据库可以支持的数据规模较小.
2. Hive 安装部署
2.1 Hive Download Link
1.Hive官网: hive.apache.org/
2.Hive文档: cwiki.apache.org/confluence/display/Hive/
3.Github: github.com/apache/hive
4.Download Link: archive.apache.org/dist/hive/
5.以apache-hive-1.2.1-bin.tar.gz 稳定版本 为实例进行安装.
1. 将apache-hive-1.2.1-bin.tar.gz上传至 linux system/opt/software目录下
[root@systemhub711 ~]# cd /opt/software/
[root@systemhub711 software]# ll
total 526728
-rw-r--r--. 1 root root 92834839 Mar 24 23:51 apache-hive-1.2.1-bin.tar.gz
-rwxrwxrwx. 1 root root 9621331 Jan 14 09:36 apache-tomcat-8.5.33.tar.gz
-rwxrwxrwx. 1 root root 212046774 Jan 24 20:37 hadoop-2.7.2.tar.gz
-rwxrwxrwx. 1 root root 189815615 Jan 14 10:22 jdk-8u162-linux-x64.tar.gz
-rwxrwxrwx. 1 root root 35042811 Jan 17 19:18 zookeeper-3.4.10.tar.gz
2. 解压apache-hive-1.2.1-bin.tar.gz 至 /opt/module/目录下
[root@systemhub711 software]# tar -zxvf apache-hive-1.2.1-bin.tar.gz -C /opt/module/
apache-hive-1.2.1-bin/NOTICE
apache-hive-1.2.1-bin/LICENSE
apache-hive-1.2.1-bin/README.txt
apache-hive-1.2.1-bin/RELEASE_NOTES.txt
apache-hive-1.2.1-bin/examples/files/emp.txt
apache-hive-1.2.1-bin/examples/files/type_evolution.avro
apache-hive-1.2.1-bin/examples/files/extrapolate_stats_partial.txt
apache-hive-1.2.1-bin/examples/files/lineitem.txt
3. 修改apache-hive-1.2.1-bin.tar.gz包名称更改为hive
[root@systemhub711 software]# cd ..
[root@systemhub711 opt]# cd module/
[root@systemhub711 module]# ll
total 20
drwxr-xr-x. 8 root root 4096 Mar 24 23:53 apache-hive-1.2.1-bin
drwxr-xr-x. 9 root root 4096 Feb 24 21:55 apache-tomcat
drwxr-xr-x. 12 root root 4096 Feb 27 14:24 hadoop
drwxr-xr-x. 8 uucp 143 4096 Dec 20 2017 jdk1.8.0_162
drwxr-xr-x. 10 1001 1001 4096 Mar 23 2017 zookeeper
[root@systemhub711 module]# mv apache-hive-1.2.1-bin hive
[root@systemhub711 module]# ll
total 20
drwxr-xr-x. 9 root root 4096 Feb 24 21:55 apache-tomcat
drwxr-xr-x. 12 root root 4096 Feb 27 14:24 hadoop
drwxr-xr-x. 8 root root 4096 Mar 24 23:53 hive
drwxr-xr-x. 8 uucp 143 4096 Dec 20 2017 jdk1.8.0_162
drwxr-xr-x. 10 1001 1001 4096 Mar 23 2017 zookeeper
[root@systemhub711 module]#
4. 修改/opt/module/hive/conf目录下hive-env.sh.template名称更改为hive-env.sh
[root@systemhub711 module]# cd /opt/module/hive/conf
[root@systemhub711 conf]# ll
total 188
-rw-rw-r--. 1 root root 1139 Apr 30 2015 beeline-log4j.properties.template
-rw-rw-r--. 1 root root 168431 Jun 19 2015 hive-default.xml.template
-rw-rw-r--. 1 root root 2378 Apr 30 2015 hive-env.sh.template
-rw-rw-r--. 1 root root 2662 Apr 30 2015 hive-exec-log4j.properties.template
-rw-rw-r--. 1 root root 3050 Apr 30 2015 hive-log4j.properties.template
-rw-rw-r--. 1 root root 1593 Apr 30 2015 ivysettings.xml
[root@systemhub711 conf]# mv hive-env.sh.template hive-env.sh
[root@systemhub711 conf]# ll
total 188
-rw-rw-r--. 1 root root 1139 Apr 30 2015 beeline-log4j.properties.template
-rw-rw-r--. 1 root root 168431 Jun 19 2015 hive-default.xml.template
-rw-rw-r--. 1 root root 2378 Apr 30 2015 hive-env.sh
-rw-rw-r--. 1 root root 2662 Apr 30 2015 hive-exec-log4j.properties.template
-rw-rw-r--. 1 root root 3050 Apr 30 2015 hive-log4j.properties.template
-rw-rw-r--. 1 root root 1593 Apr 30 2015 ivysettings.xml
[root@systemhub711 conf]#
5. 配置环境变量
在system中配置HIVE_HOME
[root@systemhub711 hive]# vim /etc/profile
## SET JAVA_HOME
JAVA_HOME=/opt/module/jdk1.8.0_162
PATH=/opt/module/jdk1.8.0_162/bin:$PATH
export JAVA_HOME PATH
## SET TOMCAT_HOME
TOMCAT_HOME=/opt/module/apache-tomcat
export TOMCAT_HOME
## SET HADOOP_HOME
HADOOP_HOME=/opt/module/hadoop
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin
## SET HIVE_HOME
export HIVE_HOME=/opt/module/hive
export PATH=$PATH:$HIVE_HOME/bin
配置完毕,按ESC,输入:wq 保存并退退出.
更新配置信息
[root@systemhub711 hive]# source /etc/profile
在hive-env.sh配置HADOOP_HOME路径 & 配置HIVE_CONF_DIR路径
[root@systemhub711 conf]# echo $HADOOP_HOME
/opt/module/hadoop
[root@systemhub711 conf]# vim hive-env.sh
配置完毕,按ESC,输入:wq 保存并退退出.
# fi
# fi
# The heap size of the jvm stared by hive shell script can be controlled via:
#
# export HADOOP_HEAPSIZE=1024
#
# Larger heap size may be required when running queries over large number of files or partitions.
# By default hive shell scripts use a heap size of 256 (MB). Larger heap size would also be
# appropriate for hive server (hwi etc).
# Set HADOOP_HOME to point to a specific hadoop install directory
export HADOOP_HOME=/opt/module/hadoop
# Hive Configuration Directory can be controlled by:
export HIVE_CONF_DIR=/opt/module/hive/conf
2.2 启动Hadoop集群
2.2.3 启动 HDFS Service
[root@systemhub511 ~]# cd /opt/module/hadoop/
[root@systemhub511 hadoop]# start-dfs.sh
2.2.4 启动 Yarn Service
[root@systemhub611 ~]# cd /opt/module/hadoop/
[root@systemhub611 hadoop]# start-yarn.sh
2.2.5 jps 查看集群进程
[root@systemhub511 hadoop]# jps
11121 NameNode
15719 Jps
11323 DataNode
15118 NodeManager
[root@systemhub511 hadoop]#
[root@systemhub611 hadoop]# jps
9106 NodeManager
5865 DataNode
10650 Jps
7629 ResourceManager
[root@systemhub611 hadoop]#
[root@systemhub711 hadoop]# jps
3089 Jps
30667 DataNode
1629 NodeManager
30974 SecondaryNameNode
[root@systemhub711 hadoop]#
2.2.6 在HDFS上创建目录
创建/tmp和/user/hive/warehouse两个目录并修改同组权限为可写
[root@systemhub611 hadoop]# hadoop fs -mkdir /tmp
[root@systemhub511 hadoop]# hadoop fs -mkdir -p /user/hive/warehouse
[root@systemhub511 hadoop]# hadoop fs -chmod 777 /tmp
[root@systemhub511 hadoop]# hadoop fs -chmod 777 /user/hive/warehouse
2.2.7 Hive基本操作
2.2.7.1 启动 Hive Service
[root@systemhub711 hive]# bin/hive
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
hive>
2.2.7.2 查看数据库
hive> show databases;
OK
default
Time taken: 0.039 seconds, Fetched: 1 row(s)
2.2.7.3 打开默认数据库
hive> use default;
OK
Time taken: 0.06 seconds
hive>
2.2.7.4 显示default数据库中的表
hive> show tables;
OK
Time taken: 0.136 seconds
hive>
2.2.7.5 创建一张表
hive> create table test(id int,name string);
OK
Time taken: 0.584 seconds
2.2.7.6 显示数据库中有几张表
hive> show tables;
OK
test
Time taken: 0.043 seconds, Fetched: 1 row(s)
hive>
2.2.7.7 查看表的结构
hive> desc test;
OK
id int
name string
Time taken: 0.181 seconds, Fetched: 2 row(s)
hive>
2.2.7.8 向表中插入数据
hive> insert into test values(1,"TestUser001");
Query ID = root_20190325104557_f36e7f6e-0254-4e4e-9216-60f08042dabb
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553477836311_0001, Tracking URL = http://systemhub611:8088/proxy/application_1553477836311_0001/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553477836311_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2019-03-25 10:46:18,198 Stage-1 map = 0%, reduce = 0%
2019-03-25 10:46:26,679 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.96 sec
MapReduce Total cumulative CPU time: 1 seconds 960 msec
Ended Job = job_1553477836311_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://systemhub511:9000/user/hive/warehouse/test/.hive-staging_hive_2019-03-25_10-45-57_843_1729594574229184317-1/-ext-10000
Loading data to table default.test
Table default.test stats: [numFiles=1, numRows=1, totalSize=14, rawDataSize=13]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.96 sec HDFS Read: 3566 HDFS Write: 82 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 960 msec
OK
Time taken: 31.477 seconds
hive>
2.2.7.9 查询表中数据
hive> select * from test;
OK
1 TestUser001
Time taken: 0.144 seconds, Fetched: 1 row(s)
hive>
2.2.7.10 退出hive
hive> quit;
[root@systemhub711 hive]#
2.2.8 Hive实操案例
2.2.8.1 将本地文件导入Hive中
需求: 将本地 /opt/module/datas/test.txt,这个目录下的数据导入到hive的test(id int, name string)数据表中.
1.数据准备
在/opt/module/目录下创建datas
在/opt/module/datas/目录下创建test.txt文件并添加数据,以Tab键间隔
[root@systemhub711 ~]# cd /opt/module/
[root@systemhub711 module]# mkdir datas
[root@systemhub711 module]# cd datas/
[root@systemhub711 datas]# touch test.txt
[root@systemhub711 datas]# ll
total 0
-rw-r--r--. 1 root root 0 Mar 25 16:02 test.txt
[root@systemhub711 datas]#
[root@systemhub711 datas]# vim test.txt
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
2.启动 Hadoop集群 & Hive
[root@systemhub711 ~]# cd /opt/module/hive/
[root@systemhub711 hive]# bin/hive
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
hive>
3.创建test001数据表,并声明文件分隔符”\t”
hive> create table test001(id int,name string) row format delimited fields terminated by "\t";
OK
Time taken: 0.222 seconds
hive>
4.将文件加载到数据表中
hive> load data local inpath "/opt/module/datas/test.txt" into table test001;
Loading data to table default.test
Table default.test stats: [numFiles=1, totalSize=56]
OK
Time taken: 0.479 seconds
hive>
5.查询数据
hive> select * from test;
OK
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 0.076 seconds, Fetched: 4 row(s)
hive>
2.2.8.2 MySql安装
1.安装包准备
查看mysql是否安装,如果安装了,卸载mysql.
[root@systemhub711 ~]# rpm -qa|grep mysql
mysql-libs-5.1.71-1.el6.x86_64
[root@systemhub711 ~]# rpm -e --nodeps mysql-libs-5.1.71-1.el6.x86_64
[root@systemhub711 ~]#
解压mysql-libs.zip文件到当前目录.
[root@systemhub711 ~]# cd /opt/software/
[root@systemhub711 software]# unzip mysql-libs.zip
Archive: mysql-libs.zip
creating: mysql-libs/
inflating: mysql-libs/MySQL-client-5.6.24-1.el6.x86_64.rpm
inflating: mysql-libs/mysql-connector-java-5.1.27.tar.gz
inflating: mysql-libs/MySQL-server-5.6.24-1.el6.x86_64.rpm
2.2.8.3 安装MySql服务端
安装mysql服务端
[root@systemhub711 mysql-libs]# rpm -ivh MySQL-server-5.6.24-1.el6.x86_64.rpm
Preparing... ########################################### [100%]
1:MySQL-server ########################################### [100%]
安装mysql客户端
[root@systemhub711 mysql-libs]# rpm -ivh MySQL-client-5.6.24-1.el6.x86_64.rpm
Preparing... ########################################### [100%]
1:MySQL-client ########################################### [100%]
[root@systemhub711 mysql-libs]#
查看产生的随机密码
[root@systemhub711 mysql-libs]# cat /root/.mysql_secret
# The random password set for the root user at Mon Mar 25 17:28:10 2019 (local time): 3krFauiObIJZG_xd
[root@systemhub711 mysql-libs]#
查看mysql状态 并 开启服务
[root@systemhub711 mysql-libs]# service mysql status
MySQL is not running [失败]
[root@systemhub711 mysql-libs]# service mysql start
Starting MySQL.. [确定]
登录mysql
[root@systemhub711 mysql-libs]# mysql -uroot -p3krFauiObIJZG_xd
Warning: Using a password on the command line interface can be insecure.
Welcome to the MySQL monitor. Commands end with ; or \g.
Your MySQL connection id is 2
Server version: 5.6.24
Copyright (c) 2000, 2015, Oracle and/or its affiliates. All rights reserved.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.
mysql>
设置自定义登录密码
mysql> set password=password("000000");
Query OK, 0 rows affected (0.01 sec)
mysql>
2.2.8.4 MySql无主机配置
配置只要是root用户+password,即可在任何主机上都能登录MySQL数据库.
登录mysql
[root@systemhub711 mysql-libs]# mysql -uroot -p
Enter password:
Welcome to the MySQL monitor. Commands end with ; or \g.
Your MySQL connection id is 3
Server version: 5.6.24 MySQL Community Server (GPL)
Copyright (c) 2000, 2015, Oracle and/or its affiliates. All rights reserved.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.
mysql>
显示当前数据库
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| mysql |
| performance_schema |
| test |
+--------------------+
4 rows in set (0.01 sec)
mysql>
进入mysql数据库
mysql> use mysql;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A
Database changed
mysql>
查询mysql数据库中的所有表
mysql> show tables;
+---------------------------+
| Tables_in_mysql |
+---------------------------+
| columns_priv |
| db |
| event |
| func |
| general_log |
| help_category |
| help_keyword |
| help_relation |
| help_topic |
| innodb_index_stats |
| innodb_table_stats |
| ndb_binlog_index |
| plugin |
| proc |
| procs_priv |
| proxies_priv |
| servers |
| slave_master_info |
| slave_relay_log_info |
| slave_worker_info |
| slow_log |
| tables_priv |
| time_zone |
| time_zone_leap_second |
| time_zone_name |
| time_zone_transition |
| time_zone_transition_type |
| user |
+---------------------------+
28 rows in set (0.00 sec)
mysql>
查询user数据表
mysql> select User, Host, Password from user;
+------+--------------+-------------------------------------------+
| User | Host | Password |
+------+--------------+-------------------------------------------+
| root | localhost | *032197AE5731D4664921A6CCAC7CFCE6A0698693 |
| root | systemhub711 | *DB2C3C3C317DE3F3FEFF12C9A60985CA29A7BBCD |
| root | 127.0.0.1 | *DB2C3C3C317DE3F3FEFF12C9A60985CA29A7BBCD |
| root | ::1 | *DB2C3C3C317DE3F3FEFF12C9A60985CA29A7BBCD |
+------+--------------+-------------------------------------------+
4 rows in set (0.01 sec)
mysql>
修改user表,把Host表内容修改为
%
mysql> update user set host='%' where host='localhost';
Query OK, 1 row affected (0.00 sec)
Rows matched: 1 Changed: 1 Warnings: 0
mysql> select User, Host, Password from user;
+------+--------------+-------------------------------------------+
| User | Host | Password |
+------+--------------+-------------------------------------------+
| root | % | *032197AE5731D4664921A6CCAC7CFCE6A0698693 |
| root | systemhub711 | *DB2C3C3C317DE3F3FEFF12C9A60985CA29A7BBCD |
| root | 127.0.0.1 | *DB2C3C3C317DE3F3FEFF12C9A60985CA29A7BBCD |
| root | ::1 | *DB2C3C3C317DE3F3FEFF12C9A60985CA29A7BBCD |
+------+--------------+-------------------------------------------+
4 rows in set (0.00 sec)
mysql>
删除root用户的其他host
mysql> delete from user where Host='systemhub711';
Query OK, 1 row affected (0.00 sec)
mysql> delete from user where Host='127.0.0.1';
Query OK, 1 row affected (0.00 sec)
mysql> delete from user where Host='::1';
Query OK, 1 row affected (0.00 sec)
mysql> select User, Host, Password from user;
+------+------+-------------------------------------------+
| User | Host | Password |
+------+------+-------------------------------------------+
| root | % | *032197AE5731D4664921A6CCAC7CFCE6A0698693 |
+------+------+-------------------------------------------+
1 row in set (0.00 sec)
mysql>
刷新
mysql> flush privileges;
Query OK, 0 rows affected (0.00 sec)
mysql>
退出
mysql> exit;
Bye
[root@systemhub711 mysql-libs]#
2.2.8.5 配置Metastore到MySql
1.在/opt/module/hive/conf目录下创建hive-site.xml配置文件.
[root@systemhub711 ~]# cd /opt/module/hive/conf/
[root@systemhub711 conf]# touch hive-site.xml
[root@systemhub711 conf]# vim hive-site.xml
2.根据官方文档配置参数,拷贝数据到hive-site.xml配置文件中 | Hive官方配置文档.
<?xml version="1.0" encoding="utf-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://systemhub711:3306/metastore?createDatabaseIfNotExist=true</value>
<description>JDBC connect string for a JDBC metastore</description>
</property>
<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC metastore</description>
</property>
<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>root</value>
<description>username to use against metastore database</description>
</property>
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>000000</value>
<description>passwordto use against metastore database</description>
</property>
</configuration>
驱动拷贝
将/opt/software/mysql-libs目录下解压mysql-connector-java-5.1.27.tar.gz驱动包.
[root@systemhub711 opt]# cd software/mysql-libs
[root@systemhub711 mysql-libs]# tar -zvxf mysql-connector-java-5.1.27.tar.gz
mysql-connector-java-5.1.27/
mysql-connector-java-5.1.27/docs/
mysql-connector-java-5.1.27/src/
mysql-connector-java-5.1.27/src/com/
mysql-connector-java-5.1.27/src/com/mysql/
mysql-connector-java-5.1.27/src/com/mysql/jdbc/
mysql-connector-java-5.1.27/src/com/mysql/jdbc/authentication/
将mysql-connector-java-5.1.27-bin.jar到/opt/module/hive/lib/
[root@systemhub711 mysql-libs]# cd mysql-connector-java-5.1.27
[root@systemhub711 mysql-connector-java-5.1.27]# ll
total 1272
-rw-r--r--. 1 root root 47173 Oct 24 2013 build.xml
-rw-r--r--. 1 root root 222520 Oct 24 2013 CHANGES
-rw-r--r--. 1 root root 18122 Oct 24 2013 COPYING
drwxr-xr-x. 2 root root 4096 Mar 25 20:56 docs
-rw-r--r--. 1 root root 872303 Oct 24 2013 mysql-connector-java-5.1.27-bin.jar
-rw-r--r--. 1 root root 61423 Oct 24 2013 README
-rw-r--r--. 1 root root 63674 Oct 24 2013 README.txt
drwxr-xr-x. 7 root root 4096 Oct 24 2013 src
[root@systemhub711 mysql-connector-java-5.1.27]# cp mysql-connector-java-5.1.27-bin.jar /opt/module/hive/lib/
[root@systemhub711 mysql-connector-java-5.1.27]# cd /opt/module/hive/lib/
[root@systemhub711 lib]# ll
-rw-r--r--. 1 root root 872303 Mar 25 20:58 mysql-connector-java-5.1.27-bin.jar
配置完毕后,先启动MySQL,查看有几个数据库,依然还是四个.
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| mysql |
| performance_schema |
| test |
+--------------------+
4 rows in set (0.01 sec)
mysql>
再次打开多个窗口,分别启动hive.
[root@systemhub711 hive]# bin/hive
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
hive>
启动hive后,回到MySQL窗口查看数据库,显示增加了metastore数据库.
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| metastore |
| mysql |
| performance_schema |
| test |
+--------------------+
5 rows in set (0.00 sec)
mysql>
去metastore数据库 喽一眼.
mysql> use metastore;
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A
Database changed
mysql> show tables;
+---------------------------+
| Tables_in_metastore |
+---------------------------+
| BUCKETING_COLS |
| CDS |
| COLUMNS_V2 |
| DATABASE_PARAMS |
| DBS |
| FUNCS |
| FUNC_RU |
| GLOBAL_PRIVS |
| PARTITIONS |
| PARTITION_KEYS |
| PARTITION_KEY_VALS |
| PARTITION_PARAMS |
| PART_COL_STATS |
| ROLES |
| SDS |
| SD_PARAMS |
| SEQUENCE_TABLE |
| SERDES |
| SERDE_PARAMS |
| SKEWED_COL_NAMES |
| SKEWED_COL_VALUE_LOC_MAP |
| SKEWED_STRING_LIST |
| SKEWED_STRING_LIST_VALUES |
| SKEWED_VALUES |
| SORT_COLS |
| TABLE_PARAMS |
| TAB_COL_STATS |
| TBLS |
| VERSION |
+---------------------------+
29 rows in set (0.00 sec)
mysql>
2.2.8.6 Hive常用交互命令
1. Hive 帮助指令
[root@systemhub711 hive]# bin/hive -help
usage: hive
-d,--define <key=value> Variable subsitution to apply to hive
commands. e.g. -d A=B or --define A=B
--database <databasename> Specify the database to use
-e <quoted-query-string> SQL from command line
-f <filename> SQL from files
-H,--help Print help information
--hiveconf <property=value> Use value for given property
--hivevar <key=value> Variable subsitution to apply to hive
commands. e.g. --hivevar A=B
-i <filename> Initialization SQL file
-S,--silent Silent mode in interactive shell
-v,--verbose Verbose mode (echo executed SQL to the
console)
[root@systemhub711 hive]#
2. -e 不进入hive交互窗口,即可执行sql语句
[root@systemhub711 hive]# bin/hive -e "select * from test";
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
OK
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 2.328 seconds, Fetched: 4 row(s)
[root@systemhub711 hive]#
3. -f 执行脚本中,执行sql语句
在/opt/module/datas目录下创建 test_hivef.hql文件
[root@systemhub711 hive]# cd ..
[root@systemhub711 module]# cd datas/
[root@systemhub711 datas]# touch test_hivef.hql
[root@systemhub711 datas]# ll
total 4
-rw-r--r--. 1 root root 0 Mar 25 22:24 test_hivef.hql
-rw-r--r--. 1 root root 56 Mar 25 16:46 test.txt
[root@systemhub711 datas]# vim test_hivef.hql
文件中写入正确的sql语句
select * from test;
执行文件中的sql语句
[root@systemhub711 datas]# cd /opt/module/hive/
[root@systemhub711 hive]# bin/hive -f /opt/module/datas/test_hivef.hql
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
OK
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 2.012 seconds, Fetched: 4 row(s)
[root@systemhub711 hive]#
执行文件中的sql语句并将结果写入文件中
[root@systemhub711 hive]# bin/hive -f /opt/module/datas/test_hivef.hql > /opt/module/datas/test_hivef_result.txt
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
OK
Time taken: 1.794 seconds, Fetched: 4 row(s)
[root@systemhub711 hive]# cat /opt/module/datas/test_hivef_result.txt
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
[root@systemhub711 hive]#
2.2.8.7 Hive 其他命令操作
2.2.8.7.1 退出hive窗口
在新版的oracle数据库中两个退出指令已经没有区别了,在以前的版本是有区别.
exit:先隐性提交数据,再退出; quit:不提交数据,退出;
[root@systemhub711 hive]# bin/hive
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
hive> quit;
[root@systemhub711 hive]#
[root@systemhub711 hive]# bin/hive
Logging initialized using configuration in jar:file:/opt/module/hive/lib/hive-common-1.2.1.jar!/hive-log4j.properties
hive> exit;
[root@systemhub711 hive]#
2.2.8.7.2 在Hive CLI命令窗口中查看hdfs文件系统
hive> dfs -ls / ;
Found 3 items
drwxr-xr-x - root supergroup 0 2019-01-27 14:35 /group
drwxrwxrwx - root supergroup 0 2019-01-25 10:09 /tmp
drwxr-xr-x - root supergroup 0 2019-01-25 09:58 /user
hive>
2.2.8.7.3 在Hive CLI命令窗口中查看hdfs本地系统
hive> ! ls /opt/module;
apache-tomcat
datas
hadoop
hive
jdk1.8.0_162
zookeeper
hive>
2.2.8.7.4 查看在Hive中输入所有历史命令
进入到当前用户的根目录执行历史命令.
[root@systemhub711 module]# cd
[root@systemhub711 ~]# cat .hivehistory
show databases;
show databases;
use default;
show tables;
create table test(id int,name string);
show tables;
insert into test(1,"TestUser001");
insert into test value(1,"TestUser001");
insert into test values(1,"TestUser001");
select * form test;
select * from test;
desc test;
quit;
load data local inpath "/opt/module/datas/test.txt" into table test;
select * form test;
select * from test;
drop table test;
create table test(id int,name string) row format delimited fields terminated by "\t";
load data local inpath "/opt/module/datas/test.txt" into table test;
select * from test;
exit;
[root@systemhub711 ~]#
2.2.8.8 Hive常见属性配置
2.2.8.8.1 Hive数据仓库位置配置
1.Default数据仓库最原始位置是在hdfs上的: /user/hive/warehouse路径下.
2.在仓库目录下,没有对默认的数据库default创建文件夹,如果某张数据表属于default数据库,直接会在数据仓库目录下创建一个文件夹.
3.修改default数据仓库原始位置.
1.在hive-site.xml配置文件中追加一下配置信息.
<property>
<name>hive.metastore.warehouse.dir</name>
<value>/user/hive/warehouse</value>
<description>location of default database for the warehouse</description>
</property>
2.2.8.8.2 配置查询后信息显示
1.在hive-site.xml文件中添加如下配置信息,就可以实现显示当前数据库,以及查询表的头信息配置.
<property>
<name>hive.cli.print.header</name>
<value>true</value>
</property>
<property>
<name>hive.cli.print.current.db</name>
<value>true</value>
</property>
2.重新启动hive,对比配置前后差异.
配置前.
hive> select * from test;
OK
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 0.808 seconds, Fetched: 4 row(s)
hive>
配置后.
hive (default)> select * from test;
OK
test.id test.name
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 1.708 seconds, Fetched: 4 row(s)
hive (default)>
2.2.8.8.3 Hive运行日志信息配置
1.Hive的log默认存放在/tmp/root/hive.log目录下(当前用户名下).
2.修改hive的log存放日志到/opt/module/hive/logs
2.1将hive-log4j.properties.template文件名称修改为hive-log4j.properties
[root@systemhub711 conf]# pwd
/opt/module/hive/conf
[root@systemhub711 conf]# ll
total 192
-rw-rw-r--. 1 root root 1139 Apr 30 2015 beeline-log4j.properties.template
-rw-rw-r--. 1 root root 168431 Jun 19 2015 hive-default.xml.template
-rw-rw-r--. 1 root root 2401 Mar 25 00:10 hive-env.sh
-rw-rw-r--. 1 root root 2662 Apr 30 2015 hive-exec-log4j.properties.template
-rw-rw-r--. 1 root root 3050 Apr 30 2015 hive-log4j.properties.template
-rw-r--r--. 1 root root 1416 Mar 25 23:11 hive-site.xml
-rw-rw-r--. 1 root root 1593 Apr 30 2015 ivysettings.xml
[root@systemhub711 conf]# mv hive-log4j.properties.template hive-log4j.properties
[root@systemhub711 conf]# ll
total 192
-rw-rw-r--. 1 root root 1139 Apr 30 2015 beeline-log4j.properties.template
-rw-rw-r--. 1 root root 168431 Jun 19 2015 hive-default.xml.template
-rw-rw-r--. 1 root root 2401 Mar 25 00:10 hive-env.sh
-rw-rw-r--. 1 root root 2662 Apr 30 2015 hive-exec-log4j.properties.template
-rw-rw-r--. 1 root root 3050 Apr 30 2015 hive-log4j.properties
-rw-r--r--. 1 root root 1416 Mar 25 23:11 hive-site.xml
-rw-rw-r--. 1 root root 1593 Apr 30 2015 ivysettings.xml
[root@systemhub711 conf]#
在hive-log4j.properties配置文件中修改log存放位置.
[root@systemhub711 conf]# vim hive-log4j.properties
-rw-r--r--. 1 root root 1416 Mar 25 23:11 hive-site.xml
-rw-rw-r--. 1 root root 1593 Apr 30 2015 ivysettings.xml
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Define some default values that can be overridden by system properties
hive.log.threshold=ALL
hive.root.logger=INFO,DRFA
hive.log.dir=/opt/module/hive/logs
2.2.8.8.4 参数配置方式
1.查看当前所有的配置信息.
hive (default)> set;
_hive.hdfs.session.path=/tmp/hive/root/3227b090-9545-485f-bae7-b9b31cadc61d
_hive.local.session.path=/tmp/root/3227b090-9545-485f-bae7-b9b31cadc61d
_hive.tmp_table_space=/tmp/hive/root/3227b090-9545-485f-bae7-b9b31cadc61d/_tmp_space.db
datanucleus.autoCreateSchema=true
datanucleus.autoStartMechanismMode=checked
datanucleus.cache.level2=false
datanucleus.cache.level2.type=none
datanucleus.connectionPoolingType=BONECP
datanucleus.fixedDatastore=false
2.参数的配置三种方式.
2.1 配置文件方式
默认配置文件: hive-default.xml
开发者自定义配置文件: hive-site.xml
注意: 开发者自定义配置会覆盖默认配置,另外,Hive也会读入Hadoop的配置,因为Hive是作为Hadoop的客户端启动的,Hive的配置会覆盖Hadoop的配置,配置文件的设定对本机启动的所有Hive进程都有效.
2.2 命令行参数方式
启动Hive时,可以在命令行添加
-hiveconf param=value来设定参数.
仅对本次hive启动有效
[root@systemhub711 hive]# bin/hive -hiveconf mapred.reduce.tasks=10
Logging initialized using configuration in file:/opt/module/hive/conf/hive-log4j.properties
hive (default)>
查看参数设置
hive (default)> set mapred.reduce.tasks;
mapred.reduce.tasks=10
hive (default)>
2.3 参数声明方式
可以在HQL中使用SET关键字设定参数.
仅对本次hive启动有效
[root@systemhub711 hive]# bin/hive -hiveconf mapred.reduce.tasks=100
Logging initialized using configuration in file:/opt/module/hive/conf/hive-log4j.properties
hive (default)>
查看参数设置
hive (default)> set mapred.reduce.tasks;
mapred.reduce.tasks=100
hive (default)>
上述三种设定方式的优先级依次递增.
即配置文件
<命令行参数<参数声明.注意某些系统级的参数,例如log4j相关的设定,必须用前两种方式设定,因为那些参数的读取在会话建立以前已经完成了.
3. Hive 数据类型
3.1 基本数据类型
对于Hive的String类型相当于数据库的varchar类型,该类型是一个可变的字符串,不过它不能声明其中最多能存储多少个字符,理论上它可以存储2GB的字符数.
| Hive 数据类型 | Java 数据类型 | 长度 | 数值 |
| TINYINT | byte | 1byte有符号整数 | 20 |
| SMALINT | short | 2byte有符号整数 | 20 |
| INT | int | 4byte有符号整数 | 20 |
| BIGINT | long | 8byte有符号整数 | 20 |
| BOOLEAN | boolean | 布尔类型,true或者false | TRUE / FALSE |
| FLOAT | float | 单精度浮点数 | 3.14159 |
| DOUBLE | double | 双精度浮点数 | 3.14159 |
| STRING | string | 字符串类型,可以指定字符集,可以使用单引号或者双引号 | ‘now is the time’ / “for all good men” |
| TIMESTAMP | 时间类型 | ||
| BINARY | 字节数组 |
3.2 集合数据类型
Hive有三种复杂数据类型ARRAY、MAP 和STRUCT,ARRAY和MAP与Java中的Array和Map类似,而STRUCT与C语言中的Struct类似,它封装了一个命名字段集合,复杂数据类型允许任意层次的嵌套.
| 数据类型 | 描述 | 语法示例 |
| STRUCT | 和c语言中的struct类似,都可以通过“点”符号访问元素内容,例如如果某个列的数据类型是STRUCT{first STRING,last STRING},那么第1个元素可以通过字段.first来引用. | struct() |
| MAP | MAP是一组键-值对元组集合,使用数组表示法可以访问数据,例如如果某个列的数据类型是MAP,其中键->值对是’first’->’John’和’last’->’Doe’,那么可以通过字段名[‘last’]获取最后一个元素. | map() |
| ARRAY | 数组是一组具有相同类型和名称的变量的集合,这些变量称为数组的元素,每个数组元素都有一个编号,编号从零开始,例如数组值为[‘John’, ‘Doe’],那么第2个元素可以通过数组名[1]进行引用. | Array() |
3.2.1 案例实操
3.2.1.1 用JSON格式来表示其数据结构.
{
"name": "TestUser",
// Array 列表
"friends": ["TestUser001" , "TestUser002"] ,
// Map 键值
"children": {
"TestUser003": "003",
"TestUser004": "004"
},
// Struct 结构
"address": {
"street": "china",
"city": "beijing"
}
}
3.2.1.2 基于上述数据结构,在Hive里创建对应的表,并导入数据.
创建本地测试文件test001.txt
注: MAP,STRUCT和ARRAY里的元素间关系都可以用同一个字符
_下划线表示.
TestUser,TestUser001_TestUser002,TestUser003:003_TestUser004:004,china_beijing
DemoUser,DemoUser001_DemoUser002,DemoUser003:003_DemoUser004:004,china_beijing
在Hive创建test测试表
SQL File
create table test001(
name string,
firends array<string>,
children map<string,int>,
address struct<street:string,city:string>
)
row format delimited fields terminated by ','
collection items terminated by '_'
map keys terminated by ':'
lines terminated by '\n';
字段解释
表示列分隔符
row format delimited fields terminated by ','
表示MAP STRUCT 和 ARRAY 分隔符(数据分割符号)
collection items terminated by '_'
表示MAP中的key与value分隔符
map keys terminated by ':'
表示行分隔符
lines terminated by '\n';
create table
hive (default)> set hive.cli.print.current.db=true;
hive (default)> create table test001(
> name string,
> firends array<string>,
> children map<string,int>,
> address struct<street:string,city:string>
> )
> row format delimited fields terminated by ','
> collection items terminated by '_'
> map keys terminated by ':'
> lines terminated by '\n';
OK
Time taken: 0.269 seconds
hive (default)> show tables;
OK
tab_name
test
test001
Time taken: 0.053 seconds, Fetched: 2 row(s)
hive (default)>
导入文本数据到测试表
hive (default)> load data local inpath '/opt/module/datas/test001.txt' into table test001;
Loading data to table default.test001
Table default.test001 stats: [numFiles=1, totalSize=158]
OK
Time taken: 1.268 seconds
hive (default)>
查询全部数据
hive (default)> select * from test001;
OK
test001.name test001.firends test001.children test001.address
TestUser ["TestUser001","TestUser002"] {"TestUser003":3,"TestUser004":4} {"street":"china","city":"beijing"}
DemoUser ["DemoUser001","DemoUser002"] {"DemoUser003":3,"DemoUser004":4} {"street":"china","city":"beijing"}
Time taken: 0.121 seconds, Fetched: 2 row(s)
hive (default)>
查询三种集合列表数据
以下分别是ARRAY / MAP / STRUCT的访问方式.
hive (default)> select firends[1],children['TestUser003'],address.city from test001 where name="TestUser";
OK
_c0 _c1 city
TestUser002 3 beijing
Time taken: 0.32 seconds, Fetched: 1 row(s)
hive (default)>
3.3 类型转化
Hive的原子数据类型是可以进行隐式转换的,类似于Java的类型转换.
例如某表达式使用INT类型,TINYINT会自动转换为INT类型,但是Hive不会进行反向转化.
例如,某表达式使用TINYINT类型,INT不会自动转换为TINYINT类型,它会返回错误,除非使用
CAST操作.
3.3.1 隐式类型转换规则
1.任何整数类型都可以隐式地转换为一个范围更广的类型,如TINYINT可以转换成INT,INT可以转换成BIGINT.
2.所有整数类型、FLOAT和STRING类型都可以隐式地转换成DOUBLE.
3.TINYINT,SMALLINT,INT都可以转换为FLOAT.
4.BOOLEAN类型不可以转换为任何其它的类型.
3.3.2 使用CAST显示进行数据类型转换
例如
CAST('1' AS INT)将把字符串'1'转换成整数1,如果强制类型转换失败,如执行CAST('X' AS INT),表达式返回空值NULL.
4. DDL 数据定义
DDL(Data Definition Language)数据库模式定义语言,是用于描述数据库中要存储的现实世界实体的语言.
4.1 创建数据库
创建一个数据库,数据库在HDFS上的默认存储路径是/user/hive/warehouse/*.db
避免要创建的数据库已经存在错误,增加if not exists判断.
hive (default)> create database if not exists hive_db;
OK
Time taken: 0.131 seconds
hive (default)>
当前创建的数据库会存放指定在HDFS路径.
4.2 查询数据库
4.2.1 显示数据库
1.显示数据库
hive (default)> show databases;
OK
database_name
default
hive_db
Time taken: 0.131 seconds, Fetched: 2 row(s)
hive (default)>
2.过滤显示查询的数据库
hive (default)> show databases like 'hive_db*';
OK
database_name
hive_db
Time taken: 0.039 seconds, Fetched: 1 row(s)
hive (default)>
4.2.2 查看数据库详情
1.显示数据库信息
hive (default)> desc database hive_db;
OK
db_name comment location owner_name owner_type parameters
hive_db hdfs://systemhub511:9000/user/hive/warehouse/hive_db.db root USER
Time taken: 0.042 seconds, Fetched: 1 row(s)
hive (default)>
2.显示数据库详细信息
hive (default)> desc database extended hive_db;
OK
db_name comment location owner_name owner_type parameters
hive_db hdfs://systemhub511:9000/user/hive/warehouse/hive_db.db root USER
Time taken: 0.027 seconds, Fetched: 1 row(s)
hive (default)>
4.2.3 切换当前数据库
hive (default)> use hive_db;
OK
Time taken: 0.028 seconds
hive (hive_db)>
4.3 修改数据库
用户可以使用
ALTER DATABASE命令为某个数据库的DBPROPERTIES设置键-值对属性值,来描述这个数据库的属性信息.数据库的其他元数据信息都是不可更改的,包括数据库名和数据库所在的目录位置.
hive (hive_db)> alter database hive_db set dbproperties('createtime'='2090-00-00');
OK
Time taken: 0.073 seconds
hive (hive_db)> desc database extended hive_db;
OK
db_name comment location owner_name owner_type parameters
hive_db hdfs://systemhub511:9000/user/hive/warehouse/hive_db.db root USER {createtime=2090-00-00}
Time taken: 0.034 seconds, Fetched: 1 row(s)
hive (hive_db)>
4.4 删除数据库
如果数据库不为空,可以采用
cascade命令,强制删除.
hive (default)> drop database hive_db cascade;
Time taken: 0.034 seconds
4.5 创建表
4.5.1 建表语法
CREATE [EXTERNAL] TABLE [IF NOT EXISTS] table_name
[(col_name data_type [COMMENT col_comment], ...)]
[COMMENT table_comment]
[PARTITIONED BY (col_name data_type [COMMENT col_comment], ...)]
[CLUSTERED BY (col_name, col_name, ...)
[SORTED BY (col_name [ASC|DESC], ...)]
INTO num_buckets BUCKETS]
[ROW FORMAT row_format]
[STORED AS file_format]
[LOCATION hdfs_path]
字段解释说明
1.
CREATE TABLE创建一个指定名字的表,如果相同名字的表已经存在,则抛出异常,用户可以用IF NOT EXISTS选项来忽略这个异常.2.
EXTERNAL关键字可以让用户创建一个外部表,在建表的同时指定一个指向实际数据的路径(LOCATION)Hive创建内部表时,会将数据移动到数据仓库指向的路径,若创建外部表,仅记录数据所在的路径,不对数据的位置做任何改变,在删除表的时候,内部表的元数据和数据会被一起删除,而外部表只删除元数据,不删除数据.3.
COMMENT: 为表和列添加注释.4.
PARTITIONED BY创建分区表.5.
CLUSTERED BY创建分桶表.6.
SORTED BY不常用.7.
ROW FORMAT DELIMITED [FIELDS TERMINATED BY char] [COLLECTIONITEMS TERMINATED BY char]
[MAP KEYS TERMINATED BY char] [LINES TERMINATED BY char] SERDE serde_name [WITH SERDEPROPERTIES (property_name=property_value, property_name=property_value, ...)]开发者在建表的时候可以自定义SerDe或者使用自带的SerDe,如果没有指定ROW FORMAT 或者ROW FORMAT DELIMITED,将会使用自带的SerDe,在建表的时候,用户还需要为表指定列,开发者在指定表的列的同时也会指定自定义的SerDe,Hive通过SerDe确定表的具体的列的数据.
8.
STORED AS指定存储文件类型.
常用的存储文件类型:SEQUENCEFILE(二进制序列文件),TEXTFILE(文本),RCFILE(列式存储格式文件),如果文件数据是纯文本,可以使用STORED AS TEXTFILE,如果数据需要压缩,使用STORED AS SEQUENCEFILE.9.
LOCATION: 指定表在HDFS上的存储位置.10.
LIKE允许开发者复制现有的表结构,但是不复制数据.
4.5.2 管理表
4.5.2.1 理论
默认创建的表都是所谓的管理表,有时也被称为内部表.
因为这种表,Hive会或多或少地制着数据的生命周期.
Hive默认情况下会将这些表的数据存储在由配置项
hive.metastore.warehouse.dir(例如/user/hive/warehouse)所定义的目录的子目录下,当删除一个管理表时,Hive也会删除这个表中数据,管理表不适合和其他工具共享数据.
4.5.2.2 案例实操
1.普通创建表.
hive (default)> create table if not exists test004(id int, name string)row format delimited fields terminated by '\t' stored as textfile location '/user/hive/warehouse/test004';
OK
Time taken: 0.098 seconds
hive (default)>
2.根据查询结果创建表(查询的结果会添加到新创建的表中).
hive (default)> show tables;
OK
tab_name
test
test001
Time taken: 0.056 seconds, Fetched: 2 row(s)
hive (default)> create table test002 as select * from test001;
Query ID = root_20190328000840_11ef1852-3fee-44f5-a4d4-a15042411c6a
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553696946343_0001, Tracking URL = http://systemhub611:8088/proxy/application_1553696946343_0001/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553696946343_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.34 sec
MapReduce Total cumulative CPU time: 1 seconds 340 msec
Ended Job = job_1553696946343_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://systemhub511:9000/user/hive/warehouse/.hive-staging_hive_2019-03-28_00-08-40_557_7477541425251721483-1/-ext-10001
Moving data to: hdfs://systemhub511:9000/user/hive/warehouse/test002
Table default.test002 stats: [numFiles=1, numRows=2, totalSize=150, rawDataSize=148]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.34 sec HDFS Read: 3913 HDFS Write: 222 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 340 msec
OK
test001.name test001.firends test001.children test001.address
Time taken: 33.974 seconds
hive (default)> show tables;
OK
tab_name
test
test001
test002
Time taken: 0.041 seconds, Fetched: 3 row(s)
hive (default)> select * from test002;
OK
test002.name test002.firends test002.children test002.address
TestUser ["TestUser001","TestUser002"] {"TestUser003":3,"TestUser004":4} {"street":"china","city":"beijing"}
DemoUser ["DemoUser001","DemoUser002"] {"DemoUser003":3,"DemoUser004":4} {"street":"china","city":"beijing"}
Time taken: 0.157 seconds, Fetched: 2 row(s)
hive (default)>
3.根据已经存在的表结构创建表.
hive (default)> create table test003 like test;
OK
Time taken: 0.116 seconds
hive (default)> select * from test003;
OK
test003.id test003.name
Time taken: 0.066 seconds
hive (default)>
4.查询表的类型.
hive (default)> desc formatted test002;
OK
col_name data_type comment
# col_name data_type comment
name string
firends array<string>
children map<string,int>
address struct<street:string,city:string>
# Detailed Table Information
Database: default
Owner: root
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://systemhub511:9000/user/hive/warehouse/test002
Table Type: MANAGED_TABLE
Table Parameters:
COLUMN_STATS_ACCURATE true
numFiles 1
numRows 2
rawDataSize 148
totalSize 150
transient_lastDdlTime 1553702954
# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: -1
Bucket Columns: []
Sort Columns: []
Storage Desc Params:
serialization.format 1
Time taken: 0.179 seconds, Fetched: 34 row(s)
hive (default)>
4.5.3 外部表
4.5.3.1 理论
因为表是外部表,所有Hive并非认为其完全拥有这份数据,删除该表并不会删除掉这份数据,不过描述表的元数据信息会被删除掉.
4.5.3.2 管理表和外部表的使用场景
每天将收集到的网站日志定期流入HDFS文本文件,在外部表(原始日志表)基础上做大量的统计分析,用到的中间表、结果表使用内部表存储,数据通过SELECT+INSERT进入内部表.
4.5.3.3 案例实操
分别创建部门和员工外部表,并向表中导入数据.
1.原始数据
dept.txt & emp.txt
[root@systemhub711 ~]# cd /opt/module/datas/
[root@systemhub711 datas]# vim dept.txt
50 ACCOUNTING 1900
60 RESEARCH 1800
70 SALES 1700
80 OPERATIONS 1700
2.建表语句
[root@systemhub711 datas]# vim emp.txt
7369 SMITH CLERKSKLD 7902 1980-12-17 800.00 20
7499 ALLTE SALESMANS 7689 1987-02-23 1600.00 300.00 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.00 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.30 30
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.00 20
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.00 30
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.00 20
创建部门表
hive (default)> create external table dept(deptid int,dname string,loc int)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.137 seconds
hive (default)>
创建员工表
hive (default)> create external table if not exists emp(empno int,ename string,job string,mgr int,hiredate string,sal double,comm double,deptno int)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.121 seconds
hive (default)>
3.向外部表中导入数据
hive (default)> load data local inpath '/opt/module/datas/dept.txt' into table dept;
Loading data to table default.dept
Table default.dept stats: [numFiles=1, totalSize=70]
OK
Time taken: 0.535 seconds
hive (default)>
hive (default)> load data local inpath '/opt/module/datas/emp.txt' into table emp;
Loading data to table default.emp
Table default.emp stats: [numFiles=1, totalSize=445]
OK
Time taken: 0.302 seconds
hive (default)>
4.查看创建的表
hive (default)> select * from dept;
OK
dept.deptid dept.dname dept.loc
50 ACCOUNTING 1900
60 RESEARCH 1800
70 SALES NULL
80 OPERATIONS 1700
Time taken: 0.098 seconds, Fetched: 4 row(s)
hive (default)> select * from emp;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 0.063 seconds, Fetched: 9 row(s)
hive (default)>
5.查看表格式化数据
hive (default)> desc formatted dept;
OK
col_name data_type comment
# col_name data_type comment
deptid int
dname string
loc int
# Detailed Table Information
Database: default
Owner: root
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://systemhub511:9000/user/hive/warehouse/dept
Table Type: EXTERNAL_TABLE
Table Parameters:
COLUMN_STATS_ACCURATE true
EXTERNAL TRUE
numFiles 1
totalSize 70
transient_lastDdlTime 1553705763
# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: -1
Bucket Columns: []
Sort Columns: []
Storage Desc Params:
field.delim \t
serialization.format \t
Time taken: 0.159 seconds, Fetched: 33 row(s)
hive (default)>
6.删除外部dept数据表,并查看结果
通过查看结果来看,只是删除了描述表的元数据信息,而不会删除该数据表的元数据.
可通过再次创建外部dept数据表,依旧可以查看到dept元数据所在.
hive (default)> create external table if not exists dept(deptid int,dname string,loc int)row format delimited fields terminated by '\t';
OK
Time taken: 0.039 seconds
hive (default)> select * from dept;
OK
dept.deptid dept.dname dept.loc
50 ACCOUNTING 1900
60 RESEARCH 1800
70 SALES NULL
80 OPERATIONS 1700
Time taken: 0.06 seconds, Fetched: 4 row(s)
hive (default)>
4.5.4 管理部与外部表相互转换
注: (‘EXTERNAL’=’TRUE’) & (‘EXTERNAL’=’FALSE’) 为固定写法,区分大小写.
查询内部表数据表类型
hive (default)> desc formatted test001;
Table Type: MANAGED_TABLE
修改内部表为外部表
alter table test001 set tblproperties('EXTERNAL'='TRUE');
查询外部表数据表类型
hive (default)> desc formatted test001;
Table Type: EXTERNAL_TABLE
修改外部表为内部表
alter table test001 set tblproperties('EXTERNAL'='FALSE');
查询内部表数据表类型
hive (default)> desc formatted test001;
Table Type: MANAGED_TABLE
4.6 分区表
分区表实际上就是对应一个HDFS文件系统上的独立的文件夹,该文件夹下是该分区所有的数据文件,Hive中的分区就是分目录,把一个大的数据集根据业务需要分割成小的数据集,在查询时通过WHERE子句中的表达式选择查询所需要的指定的分区,这样的查询效率会提高很多.
4.6.1 创建分区表语法
hive (default)> create table dept_partition(deptno int, dname string, loc string)
> partitioned by (month string)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.614 seconds
hive (default)>
4.6.2 加载数据到分区表中
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-00');
Loading data to table default.dept_partition partition (month=2020-00-00)
Partition default.dept_partition{month=2020-00-00} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 2.37 seconds
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-01');
Loading data to table default.dept_partition partition (month=2020-00-01)
Partition default.dept_partition{month=2020-00-01} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 0.701 seconds
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-02');
Loading data to table default.dept_partition partition (month=2020-00-02)
Partition default.dept_partition{month=2020-00-02} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 0.558 seconds
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-03');
Loading data to table default.dept_partition partition (month=2020-00-03)
Partition default.dept_partition{month=2020-00-03} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 0.46 seconds
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-04');
Loading data to table default.dept_partition partition (month=2020-00-04)
Partition default.dept_partition{month=2020-00-04} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 0.461 seconds
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-05');
Loading data to table default.dept_partition partition (month=2020-00-05)
Partition default.dept_partition{month=2020-00-05} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 0.422 seconds
hive (default)>
4.6.3 查询分区表中数据
4.6.3.1 单分区查询
hive (default)> select * from dept_partition where month='2020-00-05';
OK
dept_partition.deptno dept_partition.dname dept_partition.loc dept_partition.month
50 ACCOUNTING 1900 2020-00-05
60 RESEARCH 1800 2020-00-05
70 SALES 2020-00-05
80 OPERATIONS 1700 2020-00-05
Time taken: 1.186 seconds, Fetched: 4 row(s)
hive (default)>
4.6.3.2 多分区联合查询
hive (default)> select * from dept_partition where month='2020-00-05'
> union
> select * from dept_partition where month='2020-00-04'
> union
> select * from dept_partition where month='2020-00-03'
> union
> select * from dept_partition where month='2020-00-02'
> union
> select * from dept_partition where month='2020-00-01';
Query ID = root_201346_e7c134f9-1082-4bd6-9588-951592861d78
Total jobs = 4
Launching Job 1 out of 4
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553737906374_0001, Tracking URL = http://systemhub611:8088/proxy/application_1553737906374_0001/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553737906374_0001
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 50%, reduce = 0%, Cumulative CPU 1.5 sec
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 3.42 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 5.2 sec
MapReduce Total cumulative CPU time: 5 seconds 200 msec
Ended Job = job_1553737906374_0001
Launching Job 2 out of 4
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553737906374_0002, Tracking URL = http://systemhub611:8088/proxy/application_1553737906374_0002/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553737906374_0002
Hadoop job information for Stage-2: number of mappers: 2; number of reducers: 1
Stage-2 map = 0%, reduce = 0%
Stage-2 map = 100%, reduce = 0%, Cumulative CPU 2.87 sec
Stage-2 map = 100%, reduce = 100%, Cumulative CPU 4.42 sec
MapReduce Total cumulative CPU time: 4 seconds 420 msec
Ended Job = job_1553737906374_0002
Launching Job 3 out of 4
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553737906374_0003, Tracking URL = http://systemhub611:8088/proxy/application_1553737906374_0003/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553737906374_0003
Hadoop job information for Stage-3: number of mappers: 2; number of reducers: 1
Stage-3 map = 0%, reduce = 0%
Stage-3 map = 100%, reduce = 0%, Cumulative CPU 2.67 sec
Stage-3 map = 100%, reduce = 100%, Cumulative CPU 4.41 sec
MapReduce Total cumulative CPU time: 4 seconds 410 msec
Ended Job = job_1553737906374_0003
Launching Job 4 out of 4
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553737906374_0004, Tracking URL = http://systemhub611:8088/proxy/application_1553737906374_0004/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553737906374_0004
Hadoop job information for Stage-4: number of mappers: 2; number of reducers: 1
Stage-4 map = 0%, reduce = 0%
Stage-4 map = 50%, reduce = 0%, Cumulative CPU 1.21 sec
Stage-4 map = 100%, reduce = 0%, Cumulative CPU 2.61 sec
Stage-4 map = 100%, reduce = 100%, Cumulative CPU 4.5 sec
MapReduce Total cumulative CPU time: 4 seconds 500 msec
Ended Job = job_1553737906374_0004
MapReduce Jobs Launched:
Stage-Stage-1: Map: 2 Reduce: 1 Cumulative CPU: 5.2 sec HDFS Read: 15645 HDFS Write: 434 SUCCESS
Stage-Stage-2: Map: 2 Reduce: 1 Cumulative CPU: 4.42 sec HDFS Read: 14689 HDFS Write: 603 SUCCESS
Stage-Stage-3: Map: 2 Reduce: 1 Cumulative CPU: 4.41 sec HDFS Read: 14810 HDFS Write: 772 SUCCESS
Stage-Stage-4: Map: 2 Reduce: 1 Cumulative CPU: 4.5 sec HDFS Read: 15517 HDFS Write: 545 SUCCESS
Total MapReduce CPU Time Spent: 18 seconds 530 msec
OK
_u5.deptno _u5.dname _u5.loc _u5.month
50 ACCOUNTING 1900 2020-00-01
50 ACCOUNTING 1900 2020-00-02
50 ACCOUNTING 1900 2020-00-03
50 ACCOUNTING 1900 2020-00-04
50 ACCOUNTING 1900 2020-00-05
60 RESEARCH 1800 2020-00-01
60 RESEARCH 1800 2020-00-02
60 RESEARCH 1800 2020-00-03
60 RESEARCH 1800 2020-00-04
60 RESEARCH 1800 2020-00-05
70 SALES 2020-00-01
70 SALES 2020-00-02
70 SALES 2020-00-03
70 SALES 2020-00-04
70 SALES 2020-00-05
80 OPERATIONS 1700 2020-00-01
80 OPERATIONS 1700 2020-00-02
80 OPERATIONS 1700 2020-00-03
80 OPERATIONS 1700 2020-00-04
80 OPERATIONS 1700 2020-00-05
Time taken: 164.191 seconds, Fetched: 20 row(s)
hive (default)>
4.6.4 增加分区
4.6.4.1 创建单个分区
hive (default)> alter table dept_partition add partition(month='2020-00-06');
OK
Time taken: 0.501 seconds
hive (default)>
4.6.4.2 同时创建多个分区
hive (default)> alter table dept_partition add partition(month='2020-00-07') partition(month='2020-00-08');
OK
Time taken: 0.184 seconds
hive (default)>
4.6.5 删除分区
4.6.1 删除单个分区
hive (default)> alter table dept_partition drop partition(month='2020-00-06');
Dropped the partition month=2020-00-06
OK
Time taken: 0.273 seconds
hive (default)>
4.6.2 同时删除多个分区
hive (default)> alter table dept_partition drop partition(month='2020-00-07'),partition(month='2020-00-08');
Dropped the partition month=2020-00-07
Dropped the partition month=2020-00-08
OK
Time taken: 0.837 seconds
hive (default)>
4.6.6 查看分区表有多少分区
hive (default)> show partitions dept_partition;
OK
partition
month=2020-00-00
month=2020-00-01
month=2020-00-02
month=2020-00-03
month=2020-00-04
month=2020-00-05
Time taken: 0.155 seconds, Fetched: 6 row(s)
hive (default)>
4.6.7 查看分区表结构
hive (default)> desc formatted dept_partition;
OK
col_name data_type comment
# col_name data_type comment
deptno int
dname string
loc string
# Partition Information
# col_name data_type comment
month string
# Detailed Table Information
Database: default
Owner: root
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://systemhub511:9000/user/hive/warehouse/dept_partition
Table Type: MANAGED_TABLE
Table Parameters:
transient_lastDdlTime 1553753699
# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: -1
Bucket Columns: []
Sort Columns: []
Storage Desc Params:
field.delim \t
serialization.format \t
Time taken: 0.183 seconds, Fetched: 34 row(s)
hive (default)>
4.6.8 分区表注意事项
4.6.8.1 创建二级分区表
hive (default)> create table dept_partition002(deptno int, dname string, loc string)
> partitioned by (month string,day string)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.215 seconds
hive (default)>
4.6.8.2 加载数据
4.6.8.2.1 加载数据到二级分区表
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition002 partition(month='2020-00-06', day='0101');
Loading data to table default.dept_partition002 partition (month=2020-00-06, day=0101)
Partition default.dept_partition002{month=2020-00-06, day=0101} stats: [numFiles=1, numRows=0, totalSize=70, rawDataSize=0]
OK
Time taken: 1.914 seconds
hive (default)>
4.6.8.2.2 查询分区数据
hive (default)> select * from dept_partition002 where month='2020-00-06' and day='0101';
OK
dept_partition002.deptno dept_partition002.dname dept_partition002.loc dept_partition002.month dept_partition002.day
50 ACCOUNTING 1900 2020-00-06 0101
60 RESEARCH 1800 2020-00-06 0101
70 SALES 2020-00-06 0101
80 OPERATIONS 1700 2020-00-06 0101
Time taken: 0.284 seconds, Fetched: 4 row(s)
hive (default)>
4.6.8.3 将数据直接上传到分区目录,让分区表与数据产生关联的方式
4.6.8.3.1 方式一: 上传数据后修复
上传数据
hive (default)> dfs -mkdir -p /user/hive/warehouse/dept_partition/month=2020-00-10;
hive (default)>
查询数据(查询不到刚上传的数据)
hive (default)> select * from dept_partition where month='2020-00-10';
OK
dept_partition.deptno dept_partition.dname dept_partition.loc dept_partition.month
Time taken: 0.147 seconds
hive (default)>
执行修复命令
hive (default)> msck repair table dept_partition;
OK
Partitions not in metastore: dept_partition:month=2020-00-10
Repair: Added partition to metastore dept_partition:month=2020-00-10
Time taken: 0.297 seconds, Fetched: 2 row(s)
hive (default)>
4.6.8.3.2 方式二: 上传数据后添加分区
上传数据
hive (default)> dfs -mkdir -p /user/hive/warehouse/dept_partition/month=2020-00-12;
hive (default)>
执行添加分区
alter table dept_partition drop partition(month='2020-00-06',day='0101');
OK
Time taken: 0.273 seconds
hive (default)>
查询数据
hive (default)> select * from dept_partition002 where month='2020-00-06' and day='0101';
OK
dept_partition002.deptno dept_partition002.dname dept_partition002.loc dept_partition002.month dept_partition002.day
50 ACCOUNTING 1900 2020-00-06 0101
60 RESEARCH 1800 2020-00-06 0101
70 SALES 2020-00-06 0101
80 OPERATIONS 1700 2020-00-06 0101
Time taken: 0.284 seconds, Fetched: 4 row(s)
hive (default)>
4.6.8.3.3 方式三: 上传数据后load数据到分区
创建目录
hive (default)> dfs -mkdir -p /user/hive/warehouse/dept_partition/month=2020-00-06/day=0101;
hive (default)>
上传数据
hive (default)> load data local inpath '/opt/module/datas/dept.txt'into table default.dept_partition partition(month='2020-00-06', day='0101');
查询数据
hive (default)> select * from dept_partition where month='2020-00-06' and day='0101';
OK
dept_partition002.deptno dept_partition002.dname dept_partition002.loc dept_partition002.month dept_partition002.day
50 ACCOUNTING 1900 2020-00-06 0101
60 RESEARCH 1800 2020-00-06 0101
70 SALES 2020-00-06 0101
80 OPERATIONS 1700 2020-00-06 0101
Time taken: 0.284 seconds, Fetched: 4 row(s)
hive (default)>
4.7 修改表
4.7.1 重命名表
4.7.1.1 语法
ALTER TABLE table_name RENAME TO new_table_name;
4.7.1.2 实操案例
hive (default)> alter table dept_partition002 rename to dept_partition003;
OK
Time taken: 0.324 seconds
hive (default)>
4.7.2 增加/修改/替换列信息
4.7.2.1 语法
4.7.2.1.1 更新列
ALTER TABLE table_name CHANGE [COLUMN] col_old_name col_new_name column_type [COMMENT col_comment] [FIRST|AFTER column_name]
4.7.2.1.2 增加和替换列
ALTER TABLE table_name ADD|REPLACE COLUMNS (col_name data_type [COMMENT col_comment], ...)
注：ADD是代表新增一字段,字段位置在所有列后面(partition列前),REPLACE则是表示替换表中所有字段.
4.7.2.2 实操案例
4.7.2.2.1 查询表结构
hive (default)> desc dept_partition;
OK
col_name data_type comment
deptno int
dname string
loc string
month string
# Partition Information
# col_name data_type comment
month string
Time taken: 0.122 seconds, Fetched: 9 row(s)
hive (default)>
4.7.2.2.2 添加列
hive (default)> alter table dept_partition add columns(deptdesc string);
OK
Time taken: 0.133 seconds
hive (default)>
查询表结构
hive (default)> desc dept_partition;
OK
col_name data_type comment
deptno int
dname string
loc string
deptdesc string
month string
# Partition Information
# col_name data_type comment
month string
Time taken: 0.09 seconds, Fetched: 10 row(s)
hive (default)>
4.7.2.2.3 更新列
hive (default)> alter table dept_partition change column deptdesc desc int;
OK
Time taken: 0.113 seconds
hive (default)>
查询表结构
hive (default)> desc dept_partition;
OK
col_name data_type comment
deptno int
dname string
loc string
desc int
month string
# Partition Information
# col_name data_type comment
month string
Time taken: 0.093 seconds, Fetched: 10 row(s)
hive (default)>
4.7.2.2.4 替换列
hive (default)> alter table dept_partition replace columns(deptno string,dname string,loc string);
OK
Time taken: 0.084 seconds
hive (default)>
查询表结构
hive (default)> desc dept_partition;
OK
col_name data_type comment
deptno string
dname string
loc string
month string
# Partition Information
# col_name data_type comment
month string
Time taken: 0.094 seconds, Fetched: 9 row(s)
hive (default)>
4.7.2.2.5 删除表
hive (default)> drop table dept_partition003;
OK
Time taken: 0.343 seconds
hive (default)>
5. DML 数据操作
DML(Data Manipulation Language)数据操纵语言,负责对数据库对象运行数据访问工作的指令集.
5.1 数据导入
5.1.1 (Load 模式)向表中装载数据
5.1.1.1 语法
load data [local] inpath 'FilePath' [overwrite] into table TableName [partition (partcol1=val1,...)];
1.
load data: 表示加载数据.2.
local: 表示从本地加载数据到hive表,否则从HDFS加载数据到hive表.3.
inpath: 表示加载数据的路径.4.
into table: 表示加载到哪张数据表.5.
TableName: 表示具体数据表.6.
overwrite: 表示覆盖表中已有数据,否则表示追加.7.
partition: 表示上传到指定分区.
5.1.2.2 实操案例
5.1.2.2.1 创建一张数据表
hive (default)> create table test005(id string,name string)row format delimited fields terminated by '\t';
OK
Time taken: 0.081 seconds
hive (default)>
5.1.2.2.2 加载本地文件到hive
hive (default)> load data local inpath '/opt/module/datas/test.txt' into table test005;
Loading data to table default.test005
Table default.test005 stats: [numFiles=1, totalSize=56]
OK
Time taken: 0.322 seconds
hive (default)>
5.1.2.2.3 加载HDFS文件到hive中
上传文件到HDFS
hive (default)> dfs -mkdir -p /user/geekparkhub/hive;
hive (default)> dfs -put /opt/module/datas/test.txt /user/geekparkhub/hive/test.txt;
5.1.2.2.4 加载数据覆盖表中已有的数据
加载数据覆盖表中已有的数据
hive (default)> load data inpath '/user/geekparkhub/hive/test.txt' overwrite into table test005;
Loading data to table default.test005
Table default.test005 stats: [numFiles=1, numRows=0, totalSize=56, rawDataSize=0]
OK
Time taken: 0.21 seconds
hive (default)>
5.1.2 (Insert 模式) 通过查询语句向表中插入数据
5.1.2.1 基本模式插入 (根据单张表查询结果)
hive (default)> insert overwrite table dept_partition002 partition(month='2020-00-11') select id, name from dept_partition where month='2020-00-12';
5.1.2.2 多插入模式 (根据多张表查询结果)
hive (default)> from dept_partition
insert overwrite table dept_partition002 partition(month='2020-00-13')
select id, name where month='2020-00-10'
insert overwrite table dept_partition002 partition(month='2020-00-14')
select id, name where month='2020-00-10';
5.1.3 (As Select 模式) 查询语句中创建表并加载数据
根据查询结果创建表 (查询的结果会添加到新创建的表中)
create table if not exists test006 as select * from test;
5.1.4 创建表时通过Location指定加载数据路径
5.1.4.1 创建表,并指定在HDFS上的位置
hive (default)> create table test007 like test;
OK
Time taken: 0.108 seconds
hive (default)>
5.1.4.2 上传数据到hdfs上
hive (default)> dfs -put /opt/module/datas/test.txt /user/hive/warehouse/test007;
5.1.4.3 查询数据
hive (default)> select * from test007;
OK
test007.id test007.name
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 0.087 seconds, Fetched: 4 row(s)
hive (default)>
5.1.5 Import数据到指定Hive表中
先用export导出后,再将数据导入.
hive (default)> import table test006 from
> '/user/geekparkhub/export/test002';
Copying data from hdfs://systemhub511:9000/user/geekparkhub/export/test002/data
Copying file: hdfs://systemhub511:9000/user/geekparkhub/export/test002/data/test.txt
Loading data to table default.test006
OK
Time taken: 0.571 seconds
hive (default)>
5.2 数据导出
5.2.1 Insert 导出
5.2.1 将查询的结果导出到本地
hive (default)> insert overwrite local directory '/opt/module/datas/export/test'
> select * from test;
Query ID = root_20190328214019_01955e31-2364-4c10-8661-cafb4bd9bb38
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553780256887_0003, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0003/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.35 sec
MapReduce Total cumulative CPU time: 1 seconds 350 msec
Ended Job = job_1553780256887_0003
Copying data to local directory /opt/module/datas/export/test
Copying data to local directory /opt/module/datas/export/test
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.35 sec HDFS Read: 2982 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 350 msec
OK
test.id test.name
Time taken: 24.917 seconds
hive (default)>
[root@systemhub711 ~]# cat /opt/module/datas/export/test/000000_0
1TestUser001
2TestUser002
3TestUser003
4TestUser004
[root@systemhub711 ~]#
5.2.2 将查询的结果格式化导出到本地
hive (default)> insert overwrite local directory '/opt/module/datas/export/test001'
> row format delimited fields terminated by '\t'
> select * from test;
Query ID = root_20190328214116_2a678b33-dc52-4aef-97d4-7c4932d14dc2
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553780256887_0004, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0004/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0004
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.32 sec
MapReduce Total cumulative CPU time: 1 seconds 320 msec
Ended Job = job_1553780256887_0004
Copying data to local directory /opt/module/datas/export/test001
Copying data to local directory /opt/module/datas/export/test001
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.32 sec HDFS Read: 2995 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 320 msec
OK
test.id test.name
Time taken: 24.856 seconds
hive (default)>
[root@systemhub711 ~]# cat /opt/module/datas/export/test001/000000_0
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
[root@systemhub711 ~]#
5.2.3 将查询的结果导出到HDFS
hive (default)> insert overwrite directory '/user/geekparkhub/export/test'
> row format delimited fields terminated by '\t'
> select * from test;
Query ID = root_20190328214749_261af019-2978-43a9-99be-4cce6bf63837
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553780256887_0005, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0005/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.3 sec
MapReduce Total cumulative CPU time: 1 seconds 300 msec
Ended Job = job_1553780256887_0005
Stage-3 is selected by condition resolver.
Stage-2 is filtered out by condition resolver.
Stage-4 is filtered out by condition resolver.
Moving data to: hdfs://systemhub511:9000/user/geekparkhub/export/test/.hive-staging_hive_2019-03-28_21-47-49_139_9022836247083242678-1/-ext-10000
Moving data to: /user/geekparkhub/export/test
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.3 sec HDFS Read: 2983 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 300 msec
OK
test.id test.name
Time taken: 24.801 seconds
hive (default)>
hive (default)> dfs -cat /user/geekparkhub/export/test/*;
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
hive (default)>
5.2.2 Hadoop指令导出到本地
hive (default)> dfs -get /user/hive/warehouse/dept_partition/month=2020-00-00/dept.txt /opt/module/datas/test002.txt;
hive (default)>
5.2.3 Hive Shell指令导出
[root@systemhub711 hive]# bin/hive -e 'select * from test;' > /opt/module/datas/test002.txt;
Logging initialized using configuration in file:/opt/module/hive/conf/hive-log4j.properties
OK
Time taken: 2.32 seconds, Fetched: 4 row(s)
[root@systemhub711 hive]#
5.2.4 Export导出到HDFS上
hive (default)> export table test to '/user/geekparkhub/export/test002';
Copying data from file:/tmp/root/16f13154-2779-4719-89d3-3453b1468948/hive_411_6501223345710054079-1/-local-10000/_metadata
Copying file: file:/tmp/root/16f13154-2779-4719-89d3-3453b1468948/hive_411_6501223345710054079-1/-local-10000/_metadata
Copying data from hdfs://systemhub511:9000/user/hive/warehouse/test
Copying file: hdfs://systemhub511:9000/user/hive/warehouse/test/test.txt
OK
Time taken: 0.458 seconds
hive (default)>
hive (default)> dfs -cat /user/geekparkhub/export/test002/data/test.txt;
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
hive (default)>
5.3 清除表中数据 (Truncate)
注意: Truncate只能删除管理表,不能删除外部表中数据
hive (default)> select * from test007;
OK
test007.id test007.name
1 TestUser001
2 TestUser002
3 TestUser003
4 TestUser004
Time taken: 0.079 seconds, Fetched: 4 row(s)
hive (default)> truncate table test007;
OK
Time taken: 0.083 seconds
hive (default)> select * from test007;
OK
test007.id test007.name
Time taken: 0.078 seconds
hive (default)>
6. 查询
查询基本语法
[WITH CommonTableExpression (, CommonTableExpression)*]
(Note: Only available starting with Hive0.13.0)
SELECT [ALL | DISTINCT] select_expr, select_expr, ...
FROM table_reference
[WHERE where_condition]
[GROUP BY col_list]
[ORDER BY col_list]
[CLUSTER BY col_list| [DISTRIBUTE BY col_list] [SORT BY col_list]]
[LIMIT number]
6.1 基本查询 (Select…From)
6.1.1 全表和特定列查询
注意:
1.SQL 语言大小写不敏感.
2.SQL 可以写在一行或者多行.
3.关键字不能被缩写也不能分行.
4.各子句一般要分行写.
5.使用缩进提高语句的可读性.
6.1.1.1 全表查询
hive (default)> select * from emp;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0
Time taken: 0.129 seconds, Fetched: 9 row(s)
hive (default)>
6.1.1.2 选择特定列查询
hive (default)> select empno,ename from emp;
OK
empno ename
7369 SMITH
7499 ALLTE
7521 WAROS
7566 JOSSS
7654 SOCTD
7698 ADAMS
7782 JAMSK
7788 FOESS
7939 KINGS
Time taken: 0.123 seconds, Fetched: 9 row(s)
hive (default)>
6.1.2 列别名
在列名和别名之间加入关键字
AS
hive (default)> select empno as no,ename as name from emp;
OK
no name
7369 SMITH
7499 ALLTE
7521 WAROS
7566 JOSSS
7654 SOCTD
7698 ADAMS
7782 JAMSK
7788 FOESS
7939 KINGS
Time taken: 0.078 seconds, Fetched: 9 row(s)
hive (default)>
6.1.3 算术运算符
| 运算符 | 描述 |
| A + B | A和B 相加 |
| A - B | A减B |
| A * B | A和B 相乘 |
| A / B | A除以B |
| A % B | A对B取余 |
| A & B | A和B按位取与 |
| AB | A和B按位取或 |
| A+B | A和B按位取异或 |
| ~A | A按位取反 |
查询所员工薪水+1
hive (default)> select sal +1 as wage from emp;
OK
wage
801.0
1601.0
1251.18
2895.25
2853.3
25525.02
1101.0
951.0
3001.0
Time taken: 0.063 seconds, Fetched: 9 row(s)
hive (default)>
6.1.4 常用函数
6.1.4.1 求总行数 (count)
hive (default)> select count(*) cnt from emp;
Query ID = root_20190328225729_5783bab3-92a5-4a6c-b830-c0f8e7127225
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553780256887_0006, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0006/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0006
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.55 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.43 sec
MapReduce Total cumulative CPU time: 3 seconds 430 msec
Ended Job = job_1553780256887_0006
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.43 sec HDFS Read: 7647 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 430 msec
OK
cnt
9
Time taken: 42.734 seconds, Fetched: 1 row(s)
hive (default)>
6.1.4.2 求工资的最大值 (max)
hive (default)> select max(sal) max_wage from emp;
Query ID = root_20190328225946_778636eb-c34a-4a75-b8e5-68684633a0a3
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553780256887_0007, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0007/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0007
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.33 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.07 sec
MapReduce Total cumulative CPU time: 3 seconds 70 msec
Ended Job = job_1553780256887_0007
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.07 sec HDFS Read: 7836 HDFS Write: 9 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 70 msec
OK
max_wage
25524.02
Time taken: 31.112 seconds, Fetched: 1 row(s)
hive (default)>
6.1.4.3 求工资的最小值 (min)
hive (default)> select min(sal) min_wage from emp;
Query ID = root_20190328230109_5dbce45f-9887-44cc-b6fb-5aa64ac11a50
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553780256887_0008, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0008/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0008
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.52 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.42 sec
MapReduce Total cumulative CPU time: 3 seconds 420 msec
Ended Job = job_1553780256887_0008
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.42 sec HDFS Read: 7836 HDFS Write: 6 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 420 msec
OK
min_wage
800.0
Time taken: 30.23 seconds, Fetched: 1 row(s)
hive (default)>
6.1.4.4 求工资的总和 (sum)
hive (default)> select sum(sal) sum_wage from emp;
Query ID = root_20190328230304_600ecafd-b2a6-4b53-8ba8-fb9938c9f0c9
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553780256887_0010, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0010/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0010
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.36 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.13 sec
MapReduce Total cumulative CPU time: 3 seconds 130 msec
Ended Job = job_1553780256887_0010
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.13 sec HDFS Read: 7830 HDFS Write: 9 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 130 msec
OK
sum_wage
39970.75
Time taken: 33.111 seconds, Fetched: 1 row(s)
hive (default)>
6.1.4.5 求工资的平均值 (avg)
hive (default)> select avg(sal) avg_wage from emp;
Query ID = root_20190328230301_15ba0d3a-130a-42a5-9b9a-a313f187e814
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553780256887_0009, Tracking URL = http://systemhub611:8088/proxy/application_1553780256887_0009/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553780256887_0009
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.21 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.96 sec
MapReduce Total cumulative CPU time: 2 seconds 960 msec
Ended Job = job_1553780256887_0009
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 2.96 sec HDFS Read: 8011 HDFS Write: 18 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 960 msec
OK
avg_wage
4441.194444444444
Time taken: 67.764 seconds, Fetched: 1 row(s)
hive (default)>
6.1.5 Limit语句
典型的查询会返回多行数据,LIMIT子句用于限制返回的行数.
hive (default)> select * from emp limit 5;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
Time taken: 0.135 seconds, Fetched: 5 row(s)
hive (default)>
6.2 Where语句
1.使用
WHERE子句,将不满足条件的行过滤掉.
2.WHERE子句紧随FROM子句.
3.案例实操
查询出薪水大于1000的所有员工.
hive (default)> select * from emp where sal > 1000;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 0.26 seconds, Fetched: 7 row(s)
hive (default)>
6.2.1 比较运算符 (Between / In / Is Null)
下面表中描述了谓词操作符,这些操作符同样可以用于JOIN…ON和HAVING语句中.
6.2.1.1 比较运算符对照表
| 操作符 | 支持数据类型 | 描述 |
| A=B | 基本数据类型 | 如果A等于B则返回TRUE,反之返回FALSE. |
| A<=>B | 基本数据类型 | 如果A和B都为NULL,则返回TRUE,其他的和等号(=)操作符的结果一致,如果任一为NULL则结果为NULL. |
| A<>B, A!=B | 基本数据类型 | A或者B为NULL则返回NULL,如果A不等于B,则返回TRUE,反之返回FALSE. |
| A<B | 基本数据类型 | A或者B为NULL,则返回NULL,如果A小于B,则返回TRUE,反之返回FALSE. |
| A<=B | 基本数据类型 | A或者B为NULL,则返回NULL,如果A小于等于B,则返回TRUE,反之返回FALSE. |
| A>B | 基本数据类型 | A或者B为NULL,则返回NULL,如果A大于B,则返回TRUE,反之返回FALSE. |
| A>=B | 基本数据类型 | A或者B为NULL,则返回NULL,如果A大于等于B,则返回TRUE,反之返回FALSE. |
| A [NOT] BETWEEN B AND C | 基本数据类型 | 如果A,B或者C任一为NULL,则结果为NULL,如果A的值大于等于B而且小于或等于C,则结果为TRUE,反之为FALSE,如果使用NOT关键字则可达到相反的效果. |
| A IS NULL | 所有数据类型 | 如果A等于NULL,则返回TRUE,反之返回FALSE. |
| A IS NOT NULL | 所有数据类型 | 如果A不等于NULL,则返回TRUE,反之返回FALSE. |
| IN(Num1,Num2) | 所有数据类型 | 使用IN运算显示列表中的值. |
| A [NOT] LIKE B | STRING 类型 | B是一个SQL下的简单正则表达式,如果A与其匹配的话,则返回TRUE,反之返回FALSE,B的表达式说明如下:‘x%’表示A必须以字母‘x’开头,‘%x’表示A必须以字母’x’结尾,而‘%x%’表示A包含有字母’x’,可以位于开头,结尾或者字符串中间,如果使用NOT,关键字则可达到相反的效果. |
| A RLIKE B,A REGEXP B | STRING 类型 | B是一个正则表达式,如果A与其匹配，则返回TRUE,反之返回FALSE,匹配使用的是JDK中的正则表达式接口实现的,因为正则也依据其中的规则,例如,正则表达式必须和整个字符串A相匹配,而不是只需与其字符串匹配. |
6.2.1.2 案例实操
6.2.1.2.1 查询薪水等于3000的所有员工
hive (default)> select * from emp where sal = 3000;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 0.063 seconds, Fetched: 1 row(s)
hive (default)>
6.2.1.2.2 查询工资在500到1000的员工信息
hive (default)> select * from emp where sal between 500 and 1000;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
Time taken: 0.086 seconds, Fetched: 2 row(s)
hive (default)>
6.2.1.2.3 查询comm为空的所有员工信息
hive (default)> select * from emp where comm is null;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
Time taken: 0.077 seconds
hive (default)>
6.2.1.2.4 查询工资是1500和3000的员工信息
hive (default)> select * from emp where sal IN(1500,3000);
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 0.117 seconds, Fetched: 1 row(s)
hive (default)>
6.2.2 Like和RLike
1.使用
LIKE运算选择类似的值.2.选择条件可以包含字符或数字:
%代表零个或多个字符(任意个字符)
_代表一个字符3.
RLIKE子句是Hive中这个功能的一个扩展,其可以通过Java的正则表达式这个更强大的语言来指定匹配条件.
6.2.2.1 案例实操
6.2.2.1.1 查找以2开头薪水的员工信息
hive (default)> select * from emp where sal LIKE '2%';
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
Time taken: 0.134 seconds, Fetched: 3 row(s)
hive (default)>
6.2.2.1.2 查找第二个数值为2薪水的员工信息
hive (default)> select * from emp where sal LIKE '_2%';
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
Time taken: 0.075 seconds, Fetched: 1 row(s)
hive (default)>
6.2.2.1.3 查找薪水中含有2的员工信息
hive (default)> select * from emp where sal RLIKE '[2]';
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
Time taken: 0.088 seconds, Fetched: 4 row(s)
hive (default)>
6.2.3 逻辑运算符 (And / Or / Not)
| 操作符 | 含义 |
| AND | 逻辑并 |
| OR | 逻辑或 |
| NOT | 逻辑否 |
6.2.3.1 案例实操
6.2.3.1.1 查询薪水大于1000,部门是30
hive (default)> select * from emp where sal > 1000 AND deptno = 30;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
Time taken: 0.107 seconds, Fetched: 2 row(s)
hive (default)>
6.2.3.1.2 查询薪水大于1000,或者部门是30
hive (default)> select * from emp where sal > 1000 or deptno = 30;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 0.088 seconds, Fetched: 7 row(s)
hive (default)>
6.2.3.1.3 查询除了20部门和30部门以外的员工信息
hive (default)> select * from emp where deptno not IN(30,20);
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
Time taken: 0.067 seconds
hive (default)>
6.3 分组
6.3.1 Group By 语句
GROUP BY语句通常会和聚合函数一起使用,按照一个或者多个列队结果进行分组,然后对每个组执行聚合操作.1.计算emp表每个部门的平均工资.
hive (default)> select avg(sal) avg_sal from emp
> group by deptno;
Query ID = root_20190329100652_589ff409-e520-40bc-9cb4-4637d9775441
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553824894278_0001, Tracking URL = http://systemhub611:8088/proxy/application_1553824894278_0001/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553824894278_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.72 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.55 sec
MapReduce Total cumulative CPU time: 3 seconds 550 msec
Ended Job = job_1553824894278_0001
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.55 sec HDFS Read: 8446 HDFS Write: 37 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 550 msec
OK
avg_sal
5302.938571428572
1425.0900000000001
Time taken: 39.062 seconds, Fetched: 2 row(s)
hive (default)>
2.计算emp每个部门中每个岗位的最高薪水.
hive (default)> select deptno,job,avg(sal) avg_sal from emp
> group by deptno,job;
Query ID = root_20190329101147_338b3ac5-d29e-46eb-abae-d4d2a3bcc3ae
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553824894278_0002, Tracking URL = http://systemhub611:8088/proxy/application_1553824894278_0002/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553824894278_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.21 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.97 sec
MapReduce Total cumulative CPU time: 2 seconds 970 msec
Ended Job = job_1553824894278_0002
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 2.97 sec HDFS Read: 8904 HDFS Write: 182 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 970 msec
OK
deptno job avg_sal
NULL CLADDJHEW 3000.0
NULL CLAEDFDFD 950.0
NULL CLERKSKLD 800.0
NULL JDHYHDSDS 2894.25
NULL JUSHHWESD 25524.02
NULL KIHNGSEHN 1100.0
NULL MANSJUSSD 2852.3
30 SALESMANS 1600.0
30 SJDHHJDJX 1250.18
Time taken: 28.517 seconds, Fetched: 9 row(s)
hive (default)>
6.3.2 Having 语句
having与where不同点
where针对表中的列发挥作用,查询数据.
having针对查询结果中的列发挥作用,筛选数据.
where后面不能写分组函数,而having后面可以使用分组函数.
having只用于group by分组统计语句.求每个部门的平均薪水大于2000的部门
hive (default)> select deptno,avg(sal) avg_sal from emp
> group by deptno
> having avg_sal > 2000;
Query ID = root_20190329102456_d4f50a80-3441-421c-9e89-acf8665d98aa
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1553824894278_0003, Tracking URL = http://systemhub611:8088/proxy/application_1553824894278_0003/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553824894278_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
Stage-1 map = 0%, reduce = 0%
Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.2 sec
Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.65 sec
MapReduce Total cumulative CPU time: 3 seconds 650 msec
Ended Job = job_1553824894278_0003
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.65 sec HDFS Read: 8985 HDFS Write: 21 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 650 msec
OK
deptno avg_sal
NULL 5302.938571428572
Time taken: 29.14 seconds, Fetched: 1 row(s)
hive (default)>
6.4 Join语句
6.4.1 等值Join
Hive支持通常的
SQL JOIN语句,但是只支持等值连接,不支持非等值连接.
hive (default)> select e.empno,e.ename,d.dname
> from emp e join dept d
> on e.deptno=d.deptid;
Query ID = root_d5dfa88a-b9a2-4377-aebe-1328cf0b6653
Total jobs = 1
WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Execution log at: /tmp/root/aebe-1328cf0b6653.log
Starting to launch local task to process map join; maximum memory = 518979584
Dump the side-table for tag: 1 with group count: 4 into file: file:/tmp/root/c9582a8b-1e8f-446e-b899-f5114533b280/-local-10003/HashTable-Stage-3/MapJoin-mapfile01--.hashtable
Uploaded 1 File to: file:/tmp/root/c9582a8b-1e8f-446e-b899-f5114533b280/hive_2019-03-29_11-02-28_999_8089713357386975442-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile01--.hashtable (373 bytes)
End of local task; Time Taken: 1.969 sec.
Execution completed successfully
MapredLocal task succeeded
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553824894278_0004, Tracking URL = http://systemhub611:8088/proxy/application_1553824894278_0004/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553824894278_0004
Hadoop job information for Stage-3: number of mappers: 1; number of reducers: 0
Stage-3 map = 0%, reduce = 0%
Stage-3 map = 100%, reduce = 0%, Cumulative CPU 1.81 sec
MapReduce Total cumulative CPU time: 1 seconds 810 msec
Ended Job = job_1553824894278_0004
MapReduce Jobs Launched:
Stage-Stage-3: Map: 1 Cumulative CPU: 1.81 sec HDFS Read: 7157 HDFS Write: 0 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 810 msec
OK
e.empno e.ename d.dname
Time taken: 29.819 seconds
hive (default)>
6.4.2 表别名
1.好处:
使用别名可以简化查询.
使用表名前缀可以提高执行效率.案例实操
hive (default)> select e.empno,e.ename,d.dname
> from emp e join dept d
> on e.deptno=d.deptid;
6.4.3 内连接
内连接: 只有进行连接的两个表中都存在与连接条件相匹配的数据才会被保留下.
6.4.4 左外连接
左外连接: JOIN操作符左边表中符合WHERE子句的所有记录将会被返回.
hive (default)> select e.empno,e.ename,d.dname
> from emp e left join dept d
> on e.deptno=d.deptid;
Query ID = root_20190329112652_1446145a-1749-4a6f-85ad-b4576ee31e0f
Total jobs = 1
WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Execution log at: /tmp/root/root_20190329112652_1446145a-1749-4a6f-85ad-b4576ee31e0f.log
Starting to launch local task to process map join; maximum memory = 518979584
2019-03-29 11:26:59 Dump the side-table for tag: 1 with group count: 4 into file: file:/tmp/root/c9582a8b-1e8f-446e-b899-f5114533b280/hive_2019-03-29_11-26-52_365_8094669471916599336-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile11--.hashtable
2019-03-29 11:26:59 Uploaded 1 File to: file:/tmp/root/c9582a8b-1e8f-446e-b899-f5114533b280/hive_2019-03-29_11-26-52_365_8094669471916599336-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile11--.hashtable (373 bytes)
End of local task; Time Taken: 1.522 sec.
Execution completed successfully
MapredLocal task succeeded
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553824894278_0005, Tracking URL = http://systemhub611:8088/proxy/application_1553824894278_0005/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553824894278_0005
Hadoop job information for Stage-3: number of mappers: 1; number of reducers: 0
Stage-3 map = 0%, reduce = 0%
Stage-3 map = 100%, reduce = 0%, Cumulative CPU 1.44 sec
MapReduce Total cumulative CPU time: 1 seconds 440 msec
Ended Job = job_1553824894278_0005
MapReduce Jobs Launched:
Stage-Stage-3: Map: 1 Cumulative CPU: 1.44 sec HDFS Read: 7035 HDFS Write: 126 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 440 msec
OK
e.empno e.ename d.dname
7369 SMITH NULL
7499 ALLTE NULL
7521 WAROS NULL
7566 JOSSS NULL
7654 SOCTD NULL
7698 ADAMS NULL
7782 JAMSK NULL
7788 FOESS NULL
7939 KINGS NULL
Time taken: 32.565 seconds, Fetched: 9 row(s)
hive (default)>
6.4.5 右外连接
右外连接: JOIN操作符右边表中符合WHERE子句的所有记录将会被返回.
hive (default)> select e.empno,e.ename,d.dname
> from emp e right join dept d
> on e.deptno=d.deptid;
Query ID = root_20190329115001_8d324711-c545-4ea3-bef5-28cc14a11989
Total jobs = 1
19/03/29 11:50:06 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Execution log at: /tmp/root/root_20190329115001_8d324711-c545-4ea3-bef5-28cc14a11989.log
Starting to launch local task to process map join; maximum memory = 518979584
2019-03-29 11:50:08 Dump the side-table for tag: 0 with group count: 2 into file: file:/tmp/root/c9582a8b-1e8f-446e-b899-f5114533b280/hive_2019-03-29_11-50-01_720_5879451311571173295-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile20--.hashtable
2019-03-29 11:50:09 Uploaded 1 File to: file:/tmp/root/c9582a8b-1e8f-446e-b899-f5114533b280/hive_2019-03-29_11-50-01_720_5879451311571173295-1/-local-10003/HashTable-Stage-3/MapJoin-mapfile20--.hashtable (413 bytes)
End of local task; Time Taken: 1.56 sec.
Execution completed successfully
MapredLocal task succeeded
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1553824894278_0006, Tracking URL = http://systemhub611:8088/proxy/application_1553824894278_0006/
Kill Command = /opt/module/hadoop/bin/hadoop job -kill job_1553824894278_0006
Hadoop job information for Stage-3: number of mappers: 1; number of reducers: 0
Stage-3 map = 0%, reduce = 0%
Stage-3 map = 100%, reduce = 0%, Cumulative CPU 1.42 sec
MapReduce Total cumulative CPU time: 1 seconds 420 msec
Ended Job = job_1553824894278_0006
MapReduce Jobs Launched:
Stage-Stage-3: Map: 1 Cumulative CPU: 1.42 sec HDFS Read: 6640 HDFS Write: 61 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 420 msec
OK
e.empno e.ename d.dname
NULL NULL ACCOUNTING
NULL NULL RESEARCH
NULL NULL SALES
NULL NULL OPERATIONS
Time taken: 29.374 seconds, Fetched: 4 row(s)
hive (default)>
6.4.6 满外连接
满外连接: 将会返回所有表中符合WHERE语句条件的所有记录,如果任一表的指定字段没有符合条件的值的话,那么就使用NULL值替代.
hive (default)> select e.empno,e.ename,d.dname
> from emp e full join dept d
> on e.deptno=d.deptid;
Stage-Stage-1: Map: 2 Reduce: 1 Cumulative CPU: 5.24 sec HDFS Read: 13887 HDFS Write: 187 SUCCESS
Total MapReduce CPU Time Spent: 5 seconds 240 msec
OK
e.empno e.ename d.dname
7939 KINGS NULL
7788 FOESS NULL
7782 JAMSK NULL
7698 ADAMS NULL
7654 SOCTD NULL
7566 JOSSS NULL
7369 SMITH NULL
7521 WAROS NULL
7499 ALLTE NULL
NULL NULL ACCOUNTING
NULL NULL RESEARCH
NULL NULL SALES
NULL NULL OPERATIONS
Time taken: 35.59 seconds, Fetched: 13 row(s)
hive (default)>
6.4.7 多表连接
注意: 连接n个表,至少需要n-1个连接条件.
例如: 连接三个表,至少需要两个连接条件.
0.数据准备
[root@systemhub711 ~]# cd /opt/module/datas/
[root@systemhub711 datas]# vim location.txt
1700 Beijing
1800 London
1900 Tokyo
1.创建位置表
hive (default)> create table if not exists default.location(loc int,loc_name string)row format delimited fields terminated by '\t';
OK
Time taken: 0.328 seconds
hive (default)>
2.导入数据
hive (default)> load data local inpath '/opt/module/datas/location.txt' into table location;
Loading data to table default.location
Table default.location stats: [numFiles=1, totalSize=36]
OK
Time taken: 0.386 seconds
hive (default)> select * from location;
OK
location.loc location.loc_name
1700 Beijing
1800 London
1900 Tokyo
Time taken: 0.077 seconds, Fetched: 3 row(s)
hive (default)>
3.多表连接查询
hive (default)> select e.ename,d.dname,l.loc_name
> from emp e join dept d
> on e.deptno=d.deptid
> join location l
> on d.loc=l.loc;
大多数情况下,Hive会对每对JOIN连接对象启动一个MapReduce任务.
本例中会首先启动一个MapReduce job对表e和表d进行连接操作,然后会再启动一个MapReduce job将第一个MapReduce job的输出和表l;进行连接操作.
注意: 为什么不是表d和表l先进行连接操作呢? 这是因为Hive总是按照从左到右的顺序执行的.
6.4.8 笛卡尔积
1.笛卡尔集会在下面条件下产生:
省略连接条件.
连接条件无效.
所有表中的所有行互相连接.
案例实操
hive (default)> select empno, deptno from emp, dept;
Warning: Map Join MAPJOIN[7][bigTable=emp] in task 'Stage-3:MAPRED' is a cross product
Query ID = root_20190329124854_dc330707-1941-4608-8244-1d5bd9214e38
Total jobs = 1
Total MapReduce CPU Time Spent: 1 seconds 470 msec
OK
empno deptno
7369 NULL
7369 NULL
7369 NULL
7369 NULL
7499 30
7499 30
7499 30
7499 30
7521 30
6.4.9 连接谓词中不支持or
错误示范
hive (default)> select e.empno,e.ename,d.deptno from emp e join dept d on e.deptno=d.deptno or e.ename=d.ename;
FAILED: SemanticException [Error 10019]: Line 1:58 OR not supported in JOIN currently 'ename'
hive (default)>
6.5 排序
6.5.1 全局排序 (Order By)
Order By: 全局排序,一个MapReduce
6.5.1.1 使用ORDER BY 子句排序
ASC (ascend) 升序 (默认)
DESC (descend) 降序
6.5.1.2 ORDER BY 子句在SELECT语句结尾
6.5.1.3 案例实操
6.5.1.3.1 查询员工信息按工资升序排列
hive (default)> select * from emp order by sal;
Query ID = root_20190329130738_e22bdd23-0e80-43cb-8667-dde41851efa4
Total jobs = 1
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.13 sec HDFS Read: 8460 HDFS Write: 472 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 130 msec
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
Time taken: 29.233 seconds, Fetched: 9 row(s)
hive (default)>
6.5.1.3.2 查询员工信息按工资降序排列
hive (default)> select * from emp order by sal desc;
Query ID = root_20190329131750_d47ccdca-56fa-4b3f-aac1-e9d35e5e44e3
Total MapReduce CPU Time Spent: 3 seconds 450 msec
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
6.5.2 按照别名排序
按照员工薪水的2倍排序
hive (default)> select ename,sal*2 twosal from emp order by twosal;
Query ID = root_20190329132035_8bf552a3-03b8-4e33-835b-56f26c8a5a12
Total MapReduce CPU Time Spent: 3 seconds 490 msec
OK
ename twosal
SMITH 1600.0
FOESS 1900.0
JAMSK 2200.0
WAROS 2500.36
ALLTE 3200.0
SOCTD 5704.6
JOSSS 5788.5
KINGS 6000.0
ADAMS 51048.04
Time taken: 31.107 seconds, Fetched: 9 row(s)
hive (default)>
6.5.3 多列排序
按照部门和工资升序排序
hive (default)> select ename,deptno,sal from emp order by deptno,sal;
Query ID = root_20190329132255_7a5c8581-0c60-4a17-a726-5f8c1f8d8b36
Total MapReduce CPU Time Spent: 2 seconds 770 msec
OK
ename deptno sal
SMITH NULL 800.0
FOESS NULL 950.0
JAMSK NULL 1100.0
SOCTD NULL 2852.3
JOSSS NULL 2894.25
KINGS NULL 3000.0
ADAMS NULL 25524.02
WAROS 30 1250.18
ALLTE 30 1600.0
Time taken: 28.476 seconds, Fetched: 9 row(s)
hive (default)>
6.5.4 MapReduce内部排序 (Sort By)
Sort By: 每个MapReduce内部进行排序,对全局结果集来说不是排序.
1.设置reduce个数.
hive (default)> set mapreduce.job.reduces=3;
2.查看设置reduce个数.
hive (default)> set mapreduce.job.reduces;
mapreduce.job.reduces=3
hive (default)>
3.根据部门编号降序查看员工信息.
hive (default)> select * from emp sort by empno desc;
Query ID = root_20190329133608_60e6d25c-1197-4a09-8c96-df4fdbb35628
Stage-Stage-1: Map: 1 Reduce: 3 Cumulative CPU: 7.96 sec HDFS Read: 16132 HDFS Write: 472 SUCCESS
Total MapReduce CPU Time Spent: 7 seconds 960 msec
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
Time taken: 32.412 seconds, Fetched: 9 row(s)
hive (default)>
6.5.5 分区排序 (Distribute By)
Distribute By: 类似MR中partition,进行分区,结合
sort by使用.
注意,Hive要求DISTRIBUTE BY语句要写在SORT BY语句之前.
对于distribute by进行测试,一定要分配多reduce进行处理,否则无法看到distribute by的效果.
6.5.6 Cluster By
当
distribute by和sorts by字段相同时,可以使用cluster by方式.
cluster by除了具有distribute by的功能外还兼具sort by的功能,但是排序只能是倒序排序,不能指定排序规则为ASC或者DESC.
1.以下两种写法等价
hive (default)> select * from emp cluster by deptno;
hive (default)> select * from emp distribute by deptno sort by deptno;
注意: 按照部门编号分区,不一定就是固定死的数值,可以是20号和30号部门分到一个分区里面去.
6.6 分桶及抽样查询
6.6.1 分桶表数据存储
分区针对的是数据的存储路径,分桶针对的是数据文件.
分区提供一个隔离数据和优化查询的便利方式,不过并非所有的数据集都可形成合理的分区,特别是之前所提到过的要确定合适的划分大小这个疑虑.
分桶是将数据集分解成更容易管理的若干部分的另一个技术.
6.6.1.1 先创建分桶表,通过直接导入数据文件的方式
6.6.1.1.1 数据准备
[root@systemhub711 datas]# vim test_bucket.txt
1001 101
1002 102
1003 103
1004 104
1005 105
1006 106
1007 107
1008 108
1009 109
1010 110
1011 111
1012 112
1013 113
1014 114
1015 115
1016 116
6.6.1.1.2 创建分桶表
hive (default)> create table test_bucket(id int,name string)
> clustered by (id)
> into 4 buckets
> row format delimited fields terminated by '\t';
OK
Time taken: 0.138 seconds
hive (default)>
6.6.1.1.3 查看表结构
hive (default)> desc formatted test_bucket;
OK
col_name data_type comment
# col_name data_type comment
id int
name string
# Detailed Table Information
Database: default
Owner: root
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://systemhub511:9000/user/hive/warehouse/test_bucket
Table Type: MANAGED_TABLE
Table Parameters:
transient_lastDdlTime 1553865393
# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: 4
Bucket Columns: [id]
Sort Columns: []
Storage Desc Params:
field.delim \t
serialization.format \t
Time taken: 0.123 seconds, Fetched: 28 row(s)
hive (default)>
6.6.1.1.4 导入数据到分桶表中
hive (default)> load data local inpath '/opt/module/datas/test_bucket.txt' into table test_bucket;
Loading data to table default.test_bucket
Table default.test_bucket stats: [numFiles=1, totalSize=144]
OK
Time taken: 0.345 seconds
hive (default)>
6.6.1.1.5 查看创建的分桶表中是否分成4个桶
发现并没有分成4个桶,是什么原因呢
hive (default)> dfs -cat /user/hive/warehouse/test_bucket/*;
1001 101
1002 102
1003 103
1004 104
1005 105
1006 106
1007 107
1008 108
1009 109
1010 110
1011 111
1012 112
1013 113
1014 114
1015 115
1016 116
hive (default)>
6.6.1.2 创建分桶表时,数据通过子查询的方式导入
6.6.1.2.1 新建一个普通test_buck表
hive (default)> create table test_buck(id int,name string)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.084 seconds
hive (default)>
6.6.1.2.2 向普通test_buck表中导入数据
hive (default)> load data local inpath '/opt/module/datas/test_bucket.txt' into table test_buck;
Loading data to table default.test_buck
Table default.test_buck stats: [numFiles=1, totalSize=144]
OK
Time taken: 0.239 seconds
hive (default)>
6.6.1.2.3 清空test_bucket表中数据
hive (default)> truncate table test_bucket;
OK
Time taken: 0.151 seconds
hive (default)>
6.6.1.2.4 通过子查询的方式,导入数据到分桶表
hive (default)> insert into table test_bucket
> select * from test_buck;
Query ID = root_20190329213846_8d66d5f4-e16d-4725-9231-e9b9dd84c1de
Loading data to table default.test_bucket
Table default.test_bucket stats: [numFiles=1, numRows=16, totalSize=144, rawDataSize=128]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.29 sec HDFS Read: 3550 HDFS Write: 220 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 290 msec
OK
test_buck.id test_buck.name
Time taken: 20.851 seconds
hive (default)>
6.6.1.2.5 发现还是只有一个分桶
hive (default)> dfs -cat /user/hive/warehouse/test_bucket/*;
1001 101
1002 102
1003 103
1004 104
1005 105
1006 106
1007 107
1008 108
1009 109
1010 110
1011 111
1012 112
1013 113
1014 114
1015 115
1016 116
hive (default)>
6.6.1.2.6 设置分桶属性
hive (default)> set hive.enforce.bucketing=true;
hive (default)> set mapreduce.job.reduces=-1;
hive (default)> insert into table test_bucket
> select id,name from test_buck cluster by(id);
Loading data to table default.test_bucket
Table default.test_bucket stats: [numFiles=5, numRows=32, totalSize=288, rawDataSize=256]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 4 Cumulative CPU: 9.76 sec HDFS Read: 15857 HDFS Write: 444 SUCCESS
Total MapReduce CPU Time Spent: 9 seconds 760 msec
OK
id name
Time taken: 37.969 seconds
hive (default)>
6.6.1.2.7 查询分桶数据
查看分桶文件
hive (default)> dfs -ls /user/hive/warehouse/test_bucket/*;
-rwxrwxrwx 3 root supergroup 14 /user/hive/warehouse/test_bucket/000000_0
-rwxrwxrwx 3 root supergroup 36 /user/hive/warehouse/test_bucket/000001_0
-rwxrwxrwx 3 root supergroup 36 /user/hive/warehouse/test_bucket/000002_0
-rwxrwxrwx 3 root supergroup 36 /user/hive/warehouse/test_bucket/000003_0
hive (default)>
查看分桶数据
hive (default)> select * from test_bucket;
OK
test_bucket.id test_bucket.name
1001 101
1002 102
1003 103
1004 104
1005 105
1006 106
1007 107
1008 108
1009 109
1010 110
1011 111
1012 112
1013 113
1014 114
1015 115
1016 116
1004 104
1008 108
1012 112
1016 116
1001 101
1005 105
1009 109
1013 113
1002 102
1006 106
1010 110
1014 114
1003 103
1007 107
1011 111
1015 115
Time taken: 0.081 seconds, Fetched: 32 row(s)
hive (default)>
6.6.2 分桶抽样查询
对于非常大的数据集,有时开发者需要使用的是一个具有代表性的查询结果而不是全部结果,Hive可以通过对表进行抽样来满足这个需求.
6.6.2.1 查询test_bucket表中的数据
hive (default)> select * from test_bucket tablesample(bucket 1 out of 4 on id);
OK
test_bucket.id test_bucket.name
1004 104
1008 108
1012 112
1016 116
1004 104
1008 108
1012 112
1016 116
Time taken: 0.127 seconds, Fetched: 8 row(s)
hive (default)>
注:
tablesample是抽样语句,语法:TABLESAMPLE(BUCKET x OUT OF y)
x表示从哪个bucket开始抽取,例如,table总bucket数为4,tablesample(bucket 4 out of 4),表示总共抽取(4/4=)个bucket的数据,抽取第4个bucket数据.
y必须是table总bucket数的倍数或者因子,hive根据y的大小,决定抽样的比例,例如table总共分了4份,当y=2时,抽取(4/2=)2个bucket数据,当y=8时,抽取(4/8=)1/2个bucket数据.注意: x的值必须小于等于y的值,否则
FAILED: SemanticException [Error 10061]: Numerator should not be bigger than denominator in sample clause for table test_bucket
6.6.3 数据块抽样
Hive提供了另外一种按照百分比进行抽样的方式,这种是基于行数的,按照输入路径下的数据块百分比进行的抽样.
hive (default)> select * from test_buck tablesample(0.1 percent);
OK
test_buck.id test_buck.name
1001 101
1002 102
1003 103
1004 104
1005 105
1006 106
1007 107
1008 108
1009 109
1010 110
1011 111
1012 112
1013 113
1014 114
1015 115
1016 116
Time taken: 0.115 seconds, Fetched: 16 row(s)
hive (default)>
提示: 这种抽样方式不一定适用于所有的文件格式,另外这种抽样的最小抽样单元是一个HDFS数据块,因此,如果表的数据大小小于普通的块大小128M的话,那么将会返回所有行.
6.7 其他常用查询函数
6.7.1 空字段赋值
6.7.1.1 函数说明
NVL:给值为NULL的数据赋值,格式是
NVL(string1,replace_with).它的功能是如果string1为NULL,则NVL函数返回replace_with的值,否则返回string1的值,如果两个参数为NULL,则返回NULL.
6.7.1.2 数据准备 采用emp数据表
6.7.1.3 查询deptno字段为NULL,则用-1代替
hive (default)> select empno,nvl(deptno,-1) from emp;
OK
empno _c1
7369 -1
7499 30
7521 30
7566 -1
7654 -1
7698 -1
7782 -1
7788 -1
7939 -1
Time taken: 0.066 seconds, Fetched: 9 row(s)
hive (default)>
6.7.2 CASE WHEN
6.7.2.1 数据准备
user表示用户名, a&b表示部门,男女表示性别
user001 A Male
user002 A Male
user003 B Female
user004 A Female
user005 B Female
user006 B Female
6.7.2.2 需求
计算出不同部分员工性别有多少
6.7.2.3 创建本地文件并导入数据
[root@systemhub711 datas]# vim emp_sex.txt
6.7.2.4 创建表并导入数据
hive (default)> create table emp_sex(name string,dept_id string,sex string) row format delimited fields terminated by ' ';
OK
Time taken: 0.11 seconds
hive (default)> load data local inpath '/opt/module/datas/emp_sex.txt' into table emp_sex;
Loading data to table default.emp_sex
Table default.emp_sex stats: [numFiles=1, totalSize=84]
OK
Time taken: 0.255 seconds
hive (default)>
6.7.2.5 按照需求查询数据
hive (default)> select dept_id,
> sum(case sex when 'Male' then 1 else 0 end) male_count,
> sum(case sex when 'Female' then 1 else 0 end) female_count
> from emp_sex
> group by dept_id;
Query ID = root_20190329235632_478f9985-3a7f-4c56-8648-fc0c2a760985
Total MapReduce CPU Time Spent: 3 seconds 320 msec
OK
dept_id male_count female_count
A 2 1
B 1 2
6.7.3 行转列
6.7.3.0 相关函数说明
CONCAT(string A/col,string B/col...): 返回输入字符串连接后的结果,支持任意个输入字符串;
CONCAT_WS(separator,str1,str2,...): 它是一个特殊形式的CONCAT(),第一个参数剩余参数间的分隔符,分隔符可以是与剩余参数一样的字符串,如果分隔符是NULL,返回值也将为NULL,这个函数会跳过分隔符参数后的任何NULL和空字符串,分隔符将被加到被连接的字符串之间.
COLLECT_SET(col): 函数只接受基本数据类型,它的主要作用是将某字段的值进行去重汇总,产生array类型字段.
6.7.3.1 数据准备
TestUser001 白羊座 A
TestUser002 射手座 A
TestUser003 白羊座 B
TestUser004 白羊座 A
TestUser005 射手座 A
6.7.3.2 需求
把星座和血型一样的人归类到一起.
6.7.3.3 创建本地文件导入数据
vim constellation.txt
TestUser001 白羊座 A
TestUser002 射手座 A
TestUser003 白羊座 B
TestUser004 白羊座 A
TestUser005 射手座 A
6.7.3.4 创建hive表并导入数据
hive (default)> create table person_info(name string,constellaction string,blood_type string)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.17 seconds
hive (default)> load data local inpath '/opt/module/datas/constellation.txt' into table person_info;
Loading data to table default.person_info
Table default.person_info stats: [numFiles=1, totalSize=120]
OK
Time taken: 1.473 seconds
hive (default)>
6.7.3.5 按需求查询数据
hive (default)> select pi.base,concat_ws(" | ",collect_set(pi.name)) name from (select name,concat(constellaction," , ",blood_type) base from person_info) pi group by pi.base;
Query ID = root_20190331163711_a233152e-be3a-40d4-8f7b-db294c916cad
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 5.39 sec HDFS Read: 8077 HDFS Write: 123 SUCCESS
Total MapReduce CPU Time Spent: 5 seconds 390 msec
OK
pi.base name
射手座 , A TestUser002 | TestUser005
白羊座 , A TestUser001 | TestUser004
白羊座 , B TestUser003
Time taken: 50.112 seconds, Fetched: 3 row(s)
hive (default)>
6.7.4 列转行
6.7.4.0 相关函数说明
EXPLODE(col): 将hive一列中复杂的array或者map结构拆分成多行.
LATERAL VIEW:用法 :
LATERAL VIEW udtf(expression) tableAlias AS columnAlias解释 : 用于split,explode等UDTF一起使用,它能够将一列数据拆成多行数据,在此基础上可以对拆分后的数据进行聚合.
6.7.4.1 数据准备
《疑犯追踪》 悬疑,动作,科幻,剧情
《Lietome》 悬疑,警匪,动作,心理,剧情
《战狼2》 战争,动作,灾难
6.7.4.2 需求
将电影分类中的数组数据展开,结果如下
6.7.4.3 创建本地文件,导入数据
vim movie.txt
《疑犯追踪》 悬疑,动作,科幻,剧情
《Lietome》 悬疑,警匪,动作,心理,剧情
《战狼2》 战争,动作,灾难
6.7.4.4 创建hive表并导入数据
hive (default)> create table movie_info (movie string,category array<string>)
> row format delimited fields terminated by '\t'
> collection items terminated by ',';
OK
Time taken: 0.378 seconds
hive (default)> load data local inpath '/opt/module/datas/movie.txt' into table movie_info;
Loading data to table default.movie_info
Table default.movie_info stats: [numFiles=1, totalSize=131]
OK
Time taken: 0.451 seconds
hive (default)>
6.7.4.5 按需求查询数据
hive (default)> select movie,category_name from movie_info LATERAL VIEW EXPLODE(category) mv as category_name;
OK
movie category_name
《疑犯追踪》 悬疑
《疑犯追踪》 动作
《疑犯追踪》 科幻
《疑犯追踪》 剧情
《Lietome》 悬疑
《Lietome》 警匪
《Lietome》 动作
《Lietome》 心理
《Lietome》 剧情
《战狼2》 战争
《战狼2》 动作
《战狼2》 灾难
Time taken: 0.137 seconds, Fetched: 12 row(s)
hive (default)>
6.7.5 窗口函数
6.7.5.0 相关函数说明
OVER(): 指定分析函数工作的数据窗口大小,这个数据窗口大小可能会随着行的变化而变化.
CURRENT ROW: 当前行.
PRECEDINGn: 往前n行数据.
FOLLOWINGn: 往后n行数据.
UNBOUNDED:UNBOUNDED PRECEDING表示从前面的起点,而UNBOUNDED FOLLOWING表示到后面的终点.
LAG(col,n): 往前第n行数据.
LEAD(col,n): 往后第n行数据.
NTILE(n): 把有序分区中的行分发到指定数据的组中,各个组有编号,编号从1开始,对于每一行,NTILE返回此行所属的组的编号,注意:n必须为int类型.
6.7.5.1 数据准备
jack,2017-01-01,10
tony,2017-01-02,15
jack,2017-02-03,23
tony,2017-01-04,29
jack,2017-01-05,46
jack,2017-04-06,42
tony,2017-01-07,50
jack,2017-01-08,55
mart,2017-04-08,62
mart,2017-04-09,68
neil,2017-05-10,12
mart,2017-04-11,75
neil,2017-06-12,80
mart,2017-04-13,94
6.7.5.2 需求
1.查询在2017年4月份购买过的顾客及总人数.
2.查询顾客的购买明细及月购买总额.
3.上述的场景,要将cost按照日期进行累加.
4.查询顾客上次的购买时间.
5.查询前20%时间的订单信息.
6.7.5.3 创建本地文件,导入数据
vim business.txt
jack,2017-01-01,10
tony,2017-01-02,15
jack,2017-02-03,23
tony,2017-01-04,29
jack,2017-01-05,46
jack,2017-04-06,42
tony,2017-01-07,50
jack,2017-01-08,55
mart,2017-04-08,62
mart,2017-04-09,68
neil,2017-05-10,12
mart,2017-04-11,75
neil,2017-06-12,80
mart,2017-04-13,94
6.7.5.4 创建hive表并导入数据
hive (default)> create table business(name string,orderdate string,cost int)
> row format delimited fields terminated by ',';
OK
Time taken: 0.096 seconds
hive (default)>
hive (default)> load data local inpath '/opt/module/datas/business.txt' into table business;
Loading data to table default.business
Table default.business stats: [numFiles=1, totalSize=266]
OK
Time taken: 0.475 seconds
hive (default)>
6.7.5.5 按需求查询数据
6.7.5.5.1 查询在2017年4月份购买过的顾客及总人数
hive (default)> select name,count(*)over() from business where substring(orderdate,1,7)='2017-04' group by name;
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.72 sec HDFS Read: 7287 HDFS Write: 140 SUCCESS
Stage-Stage-2: Map: 1 Reduce: 1 Cumulative CPU: 3.32 sec HDFS Read: 6076 HDFS Write: 14 SUCCESS
Total MapReduce CPU Time Spent: 7 seconds 40 msec
OK
name count_window_0
mart 2
jack 2
Time taken: 65.934 seconds, Fetched: 2 row(s)
hive (default)>
6.7.5.5.2 查询顾客的购买明细及月购买总额
hive (default)> select name,orderdate,cost,sum(cost) over(partition by month(orderdate)) from business;
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 4.62 sec HDFS Read: 9051 HDFS Write: 319 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 620 msec
OK
name orderdate cost sum_window_0
jack 2017-01-01 10 205
jack 2017-01-08 55 205
tony 2017-01-07 50 205
jack 2017-01-05 46 205
tony 2017-01-04 29 205
tony 2017-01-02 15 205
jack 2017-02-03 23 23
mart 2017-04-13 94 341
jack 2017-04-06 42 341
mart 2017-04-11 75 341
mart 2017-04-09 68 341
mart 2017-04-08 62 341
neil 2017-05-10 12 12
neil 2017-06-12 80 80
Time taken: 31.257 seconds, Fetched: 14 row(s)
hive (default)>
6.7.5.5.3 上述场景中,要将cost按照日期进行累加
hive (default)> select *,sum(cost) over(sort by orderdate rows between UNBOUNDED PRECEDING and CURRENT ROW) from business;
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.32 sec HDFS Read: 8890 HDFS Write: 319 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 320 msec
OK
business.name business.orderdate business.cost sum_window_0
jack 2017-01-01 10 10
tony 2017-01-02 15 25
tony 2017-01-04 29 54
jack 2017-01-05 46 100
tony 2017-01-07 50 150
jack 2017-01-08 55 205
jack 2017-02-03 23 228
jack 2017-04-06 42 270
mart 2017-04-08 62 332
mart 2017-04-09 68 400
mart 2017-04-11 75 475
mart 2017-04-13 94 569
neil 2017-05-10 12 581
neil 2017-06-12 80 661
Time taken: 31.629 seconds, Fetched: 14 row(s)
hive (default)>
6.7.5.5.4 查看顾客上次的购买时间
hive (default)> select name,orderdate,cost,
> lag(orderdate,1,'1900-01-01') over(partition by name order by orderdate) as time1,
> lag(orderdate,2) over(partition by name order by orderdate) as time2
> from business;
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 2.9 sec HDFS Read: 9382 HDFS Write: 510 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 900 msec
OK
name orderdate cost time1 time2
jack 2017-01-01 10 1900-01-01 NULL
jack 2017-01-05 46 2017-01-01 NULL
jack 2017-01-08 55 2017-01-05 2017-01-01
jack 2017-02-03 23 2017-01-08 2017-01-05
jack 2017-04-06 42 2017-02-03 2017-01-08
mart 2017-04-08 62 1900-01-01 NULL
mart 2017-04-09 68 2017-04-08 NULL
mart 2017-04-11 75 2017-04-09 2017-04-08
mart 2017-04-13 94 2017-04-11 2017-04-09
neil 2017-05-10 12 1900-01-01 NULL
neil 2017-06-12 80 2017-05-10 NULL
tony 2017-01-02 15 1900-01-01 NULL
tony 2017-01-04 29 2017-01-02 NULL
tony 2017-01-07 50 2017-01-04 2017-01-02
Time taken: 29.147 seconds, Fetched: 14 row(s)
hive (default)>
6.7.5.5.5 查询前20%时间的订单信息
hive (default)> select * from(
> select name,orderdate,cost,ntile(5) over(order by orderdate) sorted
> from business
> ) t
> where sorted = 1;
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.92 sec HDFS Read: 8941 HDFS Write: 63 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 920 msec
OK
t.name t.orderdate t.cost t.sorted
jack 2017-01-01 10 1
tony 2017-01-02 15 1
tony 2017-01-04 29 1
Time taken: 32.417 seconds, Fetched: 3 row(s)
hive (default)>
6.7.6 Rnak
6.7.6.1 函数说明
RNAK()排序相同时会重复,总数不会变.
DENSE_RNAK()排序相同时会重复,总数会减少
ROW_NUMBER()根据顺序计算
6.7.6.2 数据准备
TestUser001 Language 87
TestUser001 Mathematics 95
TestUser001 English 68
TestUser002 Language 94
TestUser002 Mathematics 56
TestUser002 English 84
TestUser003 Language 64
TestUser003 Mathematics 86
TestUser003 English 84
TestUser004 Language 65
TestUser004 Mathematics 85
TestUser004 English 78
6.7.6.3 需求
计算每门学科成绩排名.
6.7.6.4 创建本地文件,导入数据
[root@systemhub711 datas]# vim score.txt
TestUser001 Language 87
TestUser001 Mathematics 95
TestUser001 English 68
TestUser002 Language 94
TestUser002 Mathematics 56
TestUser002 English 84
TestUser003 Language 64
TestUser003 Mathematics 86
TestUser003 English 84
TestUser004 Language 65
TestUser004 Mathematics 85
TestUser004 English 78
6.7.6.5 创建hive表并导入数据
hive (default)> create table score(name string,subject string,score int)
> row format delimited fields terminated by '\t';
OK
Time taken: 0.182 seconds
hive (default)>
hive (default)> load data local inpath '/opt/module/datas/score.txt' into table score;
Loading data to table default.score
Table default.score stats: [numFiles=1, totalSize=388]
OK
Time taken: 0.268 seconds
hive (default)>
6.7.6.6 按需求查询数据
hive (default)> select name,subject,
> RANK() OVER(partition by subject order by score desc),
> DENSE_RANK() OVER(partition by subject order by score desc),
> ROW_NUMBER() OVER(partition by subject order by score desc)
> from score;
Query ID = root_20190331222827_cfb301fb-9df9-4531-aa71-9c4414112721
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.65 sec HDFS Read: 9985 HDFS Write: 336 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 650 msec
OK
name subject rank_window_0 dense_rank_window_1 row_number_window_2
TestUser003 English 1 1 1
TestUser002 English 1 1 2
TestUser004 English 3 2 3
TestUser001 English 4 3 4
TestUser002 Language 1 1 1
TestUser001 Language 2 2 2
TestUser004 Language 3 3 3
TestUser003 Language 4 4 4
TestUser001 Mathematics 1 1 1
TestUser003 Mathematics 2 2 2
TestUser004 Mathematics 3 3 3
TestUser002 Mathematics 4 4 4
Time taken: 32.982 seconds, Fetched: 12 row(s)
hive (default)>
7. 函数
7.1 系统函数
7.1.1 查看系统自带函数
hive (default)> show functions;
OK
tab_name
!
!=
%
&
*
+
-
/
<
<=
<=>
<>
=
==
>
>=
^
abs
acos
add_months
and
array
array_contains
ascii
asin
assert_true
atan
avg
base64
between
bin
case
cbrt
ceil
ceiling
coalesce
collect_list
collect_set
compute_stats
concat
concat_ws
context_ngrams
conv
corr
cos
count
covar_pop
covar_samp
create_union
cume_dist
current_database
current_date
current_timestamp
current_user
date_add
date_format
date_sub
datediff
day
dayofmonth
decode
degrees
dense_rank
div
e
elt
encode
ewah_bitmap
ewah_bitmap_and
ewah_bitmap_empty
ewah_bitmap_or
exp
explode
factorial
field
find_in_set
first_value
floor
format_number
from_unixtime
from_utc_timestamp
get_json_object
greatest
hash
hex
histogram_numeric
hour
if
in
in_file
index
initcap
inline
instr
isnotnull
isnull
java_method
json_tuple
lag
last_day
last_value
lcase
lead
least
length
levenshtein
like
ln
locate
log
log10
log2
lower
lpad
ltrim
map
map_keys
map_values
matchpath
max
min
minute
month
months_between
named_struct
negative
next_day
ngrams
noop
noopstreaming
noopwithmap
noopwithmapstreaming
not
ntile
nvl
or
parse_url
parse_url_tuple
percent_rank
percentile
percentile_approx
pi
pmod
posexplode
positive
pow
power
printf
radians
rand
rank
reflect
reflect2
regexp
regexp_extract
regexp_replace
repeat
reverse
rlike
round
row_number
rpad
rtrim
second
sentences
shiftleft
shiftright
shiftrightunsigned
sign
sin
size
sort_array
soundex
space
split
sqrt
stack
std
stddev
stddev_pop
stddev_samp
str_to_map
struct
substr
substring
sum
tan
to_date
to_unix_timestamp
to_utc_timestamp
translate
trim
trunc
ucase
unbase64
unhex
unix_timestamp
upper
var_pop
var_samp
variance
weekofyear
when
windowingtablefunction
xpath
xpath_boolean
xpath_double
xpath_float
xpath_int
xpath_long
xpath_number
xpath_short
xpath_string
year
|
~
Time taken: 0.023 seconds, Fetched: 216 row(s)
hive (default)>
7.1.2 显示自带函数用法
hive (default)> desc function upper;
OK
tab_name
upper(str) - Returns str with all characters changed to uppercase
Time taken: 0.015 seconds, Fetched: 1 row(s)
hive (default)>
7.1.3 详细显示自带函数用法
hive (default)> desc function extended upper;
OK
tab_name
upper(str) - Returns str with all characters changed to uppercase
Synonyms: ucase
Example:
> SELECT upper('Facebook') FROM src LIMIT 1;
'FACEBOOK'
Time taken: 0.02 seconds, Fetched: 5 row(s)
hive (default)>
7.2 自定义函数
Hive自带了一些函数,比如 :
max/min等,但是数量有限,自己可以通过自定义UDF来方便的扩展.当Hive提供的内置函数无法满足开发者业务处理需要时,此时就可以考虑使用
(UDF : User-Defined Function)自定义函数.
7.2.1 自定义函数类别
根据自定义函数类别分为以下三种
7.2.1.1 UDF (User-Defined-Function)
一进一出
7.2.1.2 UDAF (User-Defined Aggregation Function)
聚集函数,多进一出,类似于:count/max/min
7.2.1.3 UDTF (User-Defined Table-Generating Functions)
一进多出,如lateral view explore()
7.2.2 官方文档地址
7.2.3 编程步骤
继承org.apache.hadoop.hive.ql.UDF
需要实现evaluate函数,evaluate函数支持重载.
在hive的命令行窗口创建函数.
添加jar /add jar linux_jar_path
创建function /create [temporary] function [dbname.]function_name AS class_name;
在hive的命令行窗口删除函数.
Drop [temporary] function [if exists] [dbname.]function_name;
注意事项 : UDF必须要有返回类型,可以返回null,但是返回类型不能为void.
7.3 自定义UDF函数
7.3.1 JetBrains IntelliJ IDEA New Maven Project | 此过程省略
7.3.2 配置 Maven pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.geekparkhub</groupId>
<artifactId>hive</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<!--https://mvnrepository.com/artifact/org.apache.hive/hive-exec -->
<dependency>
<groupId>org.apache.hive</groupId>
<artifactId>hive-exec</artifactId>
<version>1.2.1</version>
</dependency>
</dependencies>
</project>
7.3.3 Create HiveUdf.class
package com.geekparkhub.hive.hiveudf;
import org.apache.hadoop.hive.ql.exec.UDF;
/**
* Geek International Park | 极客国际公园
* GeekParkHub | 极客实验室
* Website | https://www.geekparkhub.com/
* Description | Open开放 · Creation创想 | OpenSource开放成就梦想 GeekParkHub共建前所未见
* HackerParkHub | 黑客公园枢纽
* Website | https://www.hackerparkhub.com/
* Description | 以无所畏惧的探索精神 开创未知技术与对技术的崇拜
* GeekDeveloper : JEEP-711
*
* @author system
* <p>
* HiveUdf
* <p>
*/
public class HiveUdf extends UDF {
public String evaluate(final String s) {
if (s == null) {
return null;
}
return s.toLowerCase();
}
}
7.3.4 打包上传
打成jar包上传到服务器/opt/module/jars/udf.jar
[root@systemhub711 ~]# cd /opt/module/datas/jars/
[root@systemhub711 jars]# ll
total 4
-rw-r--r--. 1 root root 2449 Apr 1 12:57 udf.jar
[root@systemhub711 jars]# pwd
/opt/module/datas/jars
[root@systemhub711 jars]#
7.3.4 将jar包添加到hive的classpath
hive (default)> add jar /opt/module/datas/jars/udf.jar;
Added [/opt/module/datas/jars/udf.jar] to class path
Added resources: [/opt/module/datas/jars/udf.jar]
hive (default)>
7.3.5 创建并关联临时函数
创建临时函数并与HiveUdf.class相互关联.
hive (default)> create temporary function udf_lower as "com.geekparkhub.hive.hiveudf.HiveUdf";
OK
Time taken: 0.892 seconds
hive (default)>
7.3.6 使用自定义函数 实现大写转换小写
未转换前
hive (default)> select emp.ename from emp;
OK
emp.ename
SMITH
ALLTE
WAROS
JOSSS
SOCTD
ADAMS
JAMSK
FOESS
KINGS
Time taken: 0.092 seconds, Fetched: 9 row(s)
hive (default)>
自定义函数,转换后
hive (default)> select udf_lower(ename) from emp;
OK
_c0
smith
allte
waros
josss
soctd
adams
jamsk
foess
kings
Time taken: 1.305 seconds, Fetched: 9 row(s)
hive (default)>
8. 压缩 & 存储
8.1 Hadoop源码编译支持Snappy压缩
8.1.1 工具准备
8.1.1.1 CentOS联网
配置CentOS能连接外网,Linux虚拟机ping www.baidu.com畅通即可.
注意: 采用root角色编译,减少文件夹权限出现问题
8.1.1.2 jar包准备(hadoop源码/JDK/Maven/Protobuf)
hadoop-2.7.2-src.tar.gz| 快速下载通道
jdk-8u144-linux-x64.tar.gz| 快速下载通道
snappy-1.1.3.tar.gz| 快速下载通道
apache-maven-3.0.5-bin.tar.gz| 快速下载通道
protobuf-2.5.0.tar.gz(序列化框架) | 快速下载通道
8.1.2 jar包安装
注意: 所有操作必须在root用户下完成.
JDK
JDK解压、配置环境变量JAVA_HOME和PATH,验证java-version(如下都需要验证是否配置成功.
验证命令 ：java -version
[root@systemhub611 ~]# java -version
java version "1.8.0_162"
Java(TM) SE Runtime Environment (build 1.8.0_162-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.162-b12, mixed mode)
[root@systemhub611 ~]#
Maven
解压tar包到指定目录
[root@systemhub611 software]# tar -zvxf apache-maven-3.0.5-bin.tar.gz -C /opt/module/
重命名
[root@systemhub611 module]# mv apache-maven-3.0.5 maven
[root@systemhub611 module]# ll
total 16
drwxr-xr-x. 6 root root 4096 Feb 4 2018 ant
drwxr-xr-x. 15 10011 10011 4096 Jan 31 13:52 hadoop
drwxr-xr-x. 6 root root 4096 Feb 3 14:54 maven
[root@systemhub611 module]#
配置环境变量
[root@systemhub611 ~]# cd /opt/module/maven/
[root@systemhub611 maven]# pwd
/opt/module/maven
[root@systemhub611 maven]# vim /etc/profile
## MAVEN_HOME
export MAVEN_HOME=/opt/module/maven
export PATH=$PATH:$MAVEN_HOME/bin
[root@systemhub611 maven]# source /etc/profile
[root@systemhub611 maven]# mvn -version
Apache Maven 3.0.5 (r01de14724cdef164cd33c7c8c2fe155faf9602da; 2013-02-19 21:51:28+0800)
Maven home: /opt/module/maven
Java version: 1.8.0_162, vendor: Oracle Corporation
Java home: /opt/devtool/jdk1.8.0_162/jre
Default locale: en_US, platform encoding: UTF-8
OS name: "linux", version: "2.6.32-754.10.1.el6.x86_64", arch: "amd64", family: "unix"
[root@corehub-001 maven]#
Protobuf
解压tar包到指定目录
[root@systemhub611 software]# tar -zvxf protobuf-2.5.0.tar.gz -C /opt/module/
重命名
[root@systemhub611 module]# mv protobuf-2.5.0 protobuf
[root@systemhub611 module]# ll
total 16
drwxr-xr-x. 6 root root 4096 Feb 4 2018 ant
drwxr-xr-x. 15 10011 10011 4096 Jan 31 13:52 hadoop
drwxr-xr-x. 6 root root 4096 Feb 3 14:54 maven
drwxr-x---. 4 109965 5000 4096 Feb 28 2013 protobuf
[root@corehub-001 module]#
配置环境变量
[root@systemhub611 ~]# cd /opt/module/protobuf/
[root@systemhub611 protobuf]# pwd
/opt/module/protobuf
[root@systemhub611 protobuf]# vim /etc/profile
## PROTOBUF_HOME
export PROTOBUF_HOME=/opt/module/protobuf
export PATH=$PATH:$PROTOBUF_HOME/bin
[root@systemhub611 protobuf]# source /etc/profile
8.1.3 编译源码
8.1.1 准备编译环境
8.1.1.1 yum install svn
[root@systemhub611 module]# yum install svn
Loaded plugins: fastestmirror, refresh-packagekit, security
Determining fastest mirrors
* base: ap.stykers.moe
* extras: mirror.jdcloud.com
* updates: mirrors.neusoft.edu.cn
base | 3.7 kB 00:00
extras
--> Finished Dependency Resolution
Dependencies Resolved
=================================================================================================================
Package Arch Version Repository Size
=================================================================================================================
Installing:
subversion x86_64 1.6.11-15.el6_7 base 2.3 M
Installing for dependencies:
perl-URI noarch 1.40-2.el6 base 117 k
Transaction Summary
=================================================================================================================
Install 2 Package(s)
Total download size: 2.4 M
Installed size: 12 M
Is this ok [y/N]: y
Downloading Packages:
(1/2): perl-URI-1.40-2.el6.noarch.rpm | 117 kB 00:00
(2/2): subversion-1.6.11-15.el6_7.x86_64.rpm | 2.3 MB 00:00
-----------------------------------------------------------------------------------------------------------------
Total 4.6 MB/s | 2.4 MB 00:00
Running rpm_check_debug
Running Transaction Test
Transaction Test Succeeded
Running Transaction
Installing : perl-URI-1.40-2.el6.noarch 1/2
Installing : subversion-1.6.11-15.el6_7.x86_64 2/2
Verifying : perl-URI-1.40-2.el6.noarch 1/2
Verifying : subversion-1.6.11-15.el6_7.x86_64 2/2
Installed:
subversion.x86_64 0:1.6.11-15.el6_7
Dependency Installed:
perl-URI.noarch 0:1.40-2.el6
Complete!
[root@systemhub611 module]#
8.1.1.2 yum install autoconf automake libtool cmake
[root@systemhub611 module]# yum install autoconf automake libtool cmake
Loaded plugins: fastestmirror, refresh-packagekit, security
Loading mirror speeds from cached hostfile
* base: ap.stykers.moe
* extras: mirror.jdcloud.com
* updates: mirrors.neusoft.edu.cn
Setting up Install Process
Resolving Dependencies
--> Running transaction check
---> Package autoconf.noarch 0:2.63-5.1.el6 will be installed
---> Package automake.noarch 0:1.11.1-4.el6 will be installed
---> Package cmake.x86_64 0:2.8.12.2-4.el6 will be installed
---> Package libtool.x86_64 0:2.2.6-15.5.el6 will be installed
--> Processing Dependency: gcc = 4.4.4 for package: libtool-2.2.6-15.5.el6.x86_64
--> Running transaction check
---> Package gcc.x86_64 0:4.4.7-23.el6 will be installed
--> Processing Dependency: libgomp = 4.4.7-23.el6 for package: gcc-4.4.7-
--> Finished Dependency Resolution
Dependencies Resolved
=================================================================================================================
Package Arch Version Repository Size
=================================================================================================================
Installing:
autoconf noarch 2.63-5.1.el6 base 781 k
automake noarch 1.11.1-4.el6 base 550 k
cmake x86_64 2.8.12.2-4.el6 base 8.0 M
Transaction Summary
=================================================================================================================
Install 9 Package(s)
Upgrade 2 Package(s)
Total download size: 25 M
Is this ok [y/N]: y
Downloading Packages:
http://ap.stykers.moe/centos/6.10/os/x86_64/Packages/autoconf-2.63-5.1.el6.noarch.rpm: [Errno 14] PYCURL ERROR 6 - "Couldn't resolve host 'ap.stykers.moe'"
Trying other mirror.
http://mirrors.neusoft.edu.cn/centos/6.10/os/x86_64/Packages/autoconf-2.63-5.1.el6.noarch.rpm: [Errno 14] PYCURL ERROR 6 - "Couldn't resolve host 'mirrors.neusoft.edu.cn'"
Trying other mirror.
(1/11): autoconf-2.63-5.1.el6.noarch.rpm | 781 kB 00:02
(2/11): automake-1.11.1-4.el6.noarch.rpm | 550 kB 00:00 | 1.3 MB 00:00
-----------------------------------------------------------------------------------------------------------------
Total 485 kB/s | 25 MB 00:53
Running rpm_check_debug
Running Transaction Test
Transaction Test Succeeded
Running Transaction
Updating : libgcc-4.4.7-23.el6.x86_64 1/13
Installing : autoconf-2.63-5.1.el6.noarch 2/13
Installing : automake-1.11.1-4.el6.noarch 3/13
Installed:
autoconf.noarch 0:2.63-5.1.el6 automake.noarch 0:1.11.1-4.el6 cmake.x86_64 0:2.8.12.2-4.el6
libtool.x86_64 0:2.2.6-15.5.el6
Dependency Installed:
cloog-ppl.x86_64 0:0.15.7-1.2.el6 cpp.x86_64 0:4.4.7-23.el6 gcc.x86_64 0:4.4.7-23.el6
mpfr.x86_64 0:2.4.1-6.el6 ppl.x86_64 0:0.10.2-11.el6
Dependency Updated:
libgcc.x86_64 0:4.4.7-23.el6 libgomp.x86_64 0:4.4.7-23.el6
Complete!
[root@systemhub611 module]#
8.1.1.3 yum install ncurses-devel
[root@systemhub611 module]# yum install ncurses-devel
Loaded plugins: fastestmirror, refresh-packagekit, security
Loading mirror speeds from cached hostfile
* base: ap.stykers.moe
* extras: mirror.jdcloud.com
* updates: mirrors.neusoft.edu.cn
Setting up Install Process
Resolving Dependencies
--> Running transaction check
---> Package ncurses-base.x86_64 0:5.7-4.20090207.el6 will be an update
--> Finished Dependency Resolution
Dependencies Resolved
=================================================================================================================
Package Arch Version Repository Size
=================================================================================================================
Installing:
ncurses-devel x86_64 5.7-4.20090207.el6 base 641 k
Updating for dependencies:
ncurses-base x86_64 5.7-4.20090207.el6 base 61 k
ncurses-libs x86_64 5.7-4.20090207.el6 base 245 k
Transaction Summary
=================================================================================================================
Install 1 Package(s)
Upgrade 2 Package(s)
Total download size: 947 k
Is this ok [y/N]: y
Downloading Packages:
(1/3): ncurses-base-5.7-4.20090207.el6.x86_64.rpm | 61 kB 00:00
(2/3): ncurses-devel-5.7-4.20090207.el6.x86_64.rpm | 641 kB 00:00
(3/3): ncurses-libs-5.7-4.20090207.el6.x86_64.rpm | 245 kB 00:00
-----------------------------------------------------------------------------------------------------------------
Total 2.3 MB/s | 947 kB 00:00
Running rpm_check_debug
Running Transaction Test
Transaction Test Succeeded
Running Transaction
Updating : ncurses-base-5.7-4.20090207.el6.x86_64 1/5
Installed:
ncurses-devel.x86_64 0:5.7-4.20090207.el6
Dependency Updated:
ncurses-base.x86_64 0:5.7-4.20090207.el6 ncurses-libs.x86_64 0:5.7-4.20090207.el6
Complete!
[root@systemhub611 module]#
8.1.1.4 yum install openssl-devel
[root@systemhub611 module]# yum install openssl-devel
Loaded plugins: fastestmirror, refresh-packagekit, security
Loading mirror speeds from cached hostfile
* base: ap.stykers.moe
* extras: mirror.jdcloud.com
* updates: mirrors.neusoft.edu.cn
Setting up Install Process
Resolving Dependencies
--> Running transaction check
---> Package openssl.x86_64 0:1.0.1e-15.el6 will be updated
---> Package openssl.x86_64 0:1.0.1e-57.el6 will be an update
---> Package zlib-devel.x86_64 0:1.2.3-29.el6 will be installed
--> Running transaction check
---> Package keyutils-libs-devel.x86_64 0:1.4-5.el6 will be installed
--> Processing Dependency: keyutils-libs = 1.4-5.el6 for package: keyutils-libs-devel-1.4-5.el6.x86_64
---> Package krb5-libs.x86_64 0:1.10.3-10.el6_4.6 will be updated
--> Finished Dependency Resolution
Dependencies Resolved
=================================================================================================================
Package Arch Version Repository Size
=================================================================================================================
Installing:
openssl-devel x86_64 1.0.1e-57.el6 base 1.2 M
Installing for dependencies:
keyutils-libs-devel x86_64 1.4-5.el6 base 29 k
krb5-devel x86_64 1.10.3-65.el6
Transaction Summary
=================================================================================================================
Install 8 Package(s)
Upgrade 12 Package(s)
Total download size: 6.3 M
Is this ok [y/N]: y
Downloading Packages:
(1/20): e2fsprogs-1.41.12-24.el6.x86_64.rpm | 554 kB 00:00
(2/20): e2fsprogs-libs-1.41.12-24.el6.x86_64.rpm | 121 kB 00:00
(3/20): keyutils-1.4-5.el6.x86_64.rpm 9/32
Verifying : keyutils-1.4-5.el6.x86_64 10/32
Verifying : e2fsprogs-libs-1.41.12-24.el6.x86_64 11/32
Verifying : libselinux-python-2.0.94-7.el6.x86_64 12/32
Installed:
openssl-devel.x86_64 0:1.0.1e-57.el6
Dependency Installed:
keyutils-libs-devel.x86_64 0:1.4-5.el6 krb5-devel.x86_64 0:1.10.3-65.el6
libcom_err-devel.x86_64 0:1.41.12-24.el6
Complete!
[root@systemhub611 module]#
8.1.1.5 yum install gcc*
[root@systemhub611 module]# yum install gcc*
Loaded plugins: fastestmirror, refresh-packagekit, security
Loading mirror speeds from cached hostfile
---> Package java_cup.x86_64 1:0.10k-5.el6 will be installed
--> Finished Dependency Resolution
Dependencies Resolved
=================================================================================================================
Package Arch Version Repository Size
=================================================================================================================
Installing:
gcc-c++ x86_64 4.4.7-23.el6 base 4.7 M
gcc-gfortran x86_64 4.4.7-23.el6 base 4.7 M
gcc-gnat x86_64 4.4.7-23.el6
Transaction Summary
=================================================================================================================
Install 17 Package(s)
Upgrade 1 Package(s)
Total download size: 61 M
Is this ok [y/N]: y
Downloading Packages:
(1/18): ecj-4.5.2-3.el6.x86_64.rpm | 3.9 MB 00:00
(2/18): gcc-c++-4.4.7-23.el6.x86_64.rpm | 139 kB 00:00
(9/18): java_cup-0.10k-5.el6.x86_64.rpm
-----------------------------------------------------------------------------------------------------------------
Total 6.3 MB/s | 61 MB 00:09
Running rpm_check_debug
Running Transaction Test
Transaction Test Succeeded
Running Transaction
Installing : libgcj-4.4.7-23.el6.x86_64 1/19
Installing : libgnat-4.4.7-23.el6.x86_64 2/19
Updating : libstdc++-4.4.7-23.el6.x86_64 3/19
Installing : libstdc++-devel-4.4.7-23.el6.x86_64 4/19
Installing : gcc-c++-4.4.7-23.el6.x86_64 5/19
Installing : libgnat-devel-4.4.7-23.el6.x86_64 6/19 14/19
Installing : gcc-objc++-4.4.7-23.el6.x86_64 15/19
Installing : gcc-gfortran-4.4.7-23.el6.x86_64 16/19
Installing : gcc-java-4.4.7-23.el6.x86_64 17/19
Installing : gcc-gnat-4.4.7-23.el6.x86_64 18/19
Cleanup : libstdc++-4.4.7-4.el6.x86_64 19/19
Verifying : libobjc-4.4.7-23.el6.x86_64 1/19
Verifying : gcc-java-4.4.7-23.el6.x86_64 2/19
Verifying : libgfortran-4.4.7-23.el6.x86_64 3/19 18/19
Verifying : libstdc++-4.4.7-4.el6.x86_64 19/19
Installed:
gcc-c++.x86_64 0:4.4.7-23.el6 gcc-gfortran.x86_64 0:4.4.7-23.el6 gcc-gnat.x86_64 0:4.4.7-23.el6
gcc-java.x86_64 0:4.4.7-23.el6 gcc-objc.x86_64 0:4.4.7-23.el6 gcc-objc++.x86_64 0:4.4.7-23.el6
Dependency Installed:
ecj.x86_64 1:4.5.2-3.el6 java-1.5.0-gcj.x86_64 0:1.5.0.0-29.1.el6
java_cup.x86_64 1:0.10k-5.el6 libgcj.x86_64 0:4.4.7-23.el6
Dependency Updated:
libstdc++.x86_64 0:4.4.7-23.el6
Complete!
[root@systemhub611 module]#
8.1.12 编译安装 snappy
[root@systemhub611 software]# tar -zvxf snappy-1.1.3.tar.gz -C /opt/module/
snappy-1.1.3/
snappy-1.1.3/snappy-sinksource.cc
snappy-1.1.3/configure
snappy-1.1.3/config.guess
[root@systemhub611 snappy]# ./configure
checking for a BSD-compatible install... /usr/bin/install -c
checking whether build environment is sane... yes
checking for a thread-safe mkdir -p... /bin/mkdir -p
checking for gawk... gawk
checking whether make sets $(MAKE)... yes
checking whether make supports nested variables... yes
checking build system type... x86_64-unknown-linux-gnu
checking host system type... x86_64-unknown-linux-gnu
checking how to print strings... printf
checking for style of include used by make... GNU
checking for gcc... gcc
checking whether the C compiler works... yes
checking for C compiler default output file name... a.out
checking for suffix of executables...
checking whether we are cross compiling... no
checking for suffix of object files... o
[root@systemhub611 snappy]# make
make all-am
make[1]: Entering directory `/opt/module/snappy'
/bin/sh ./libtool --tag=CXX --mode=compile g++ -DHAVE_CONFIG_H -I. -g -O2 -MT snappy.lo -MD -MP -MF .deps/snappy.Tpo -c -o snappy.lo snappy.cc
libtool: compile: g++ -DHAVE_CONFIG_H -I. -g -O2 -MT snappy.lo -MD -MP -MF .deps/snappy.Tpo -c snappy.cc -fPIC -DPIC -o .libs/snappy.o
libtool: compile: g++ -DHAVE_CONFIG_H -I. -g -O2 -MT snappy.lo -MD -MP -MF .deps/snappy.Tpo -c snappy.cc -o snappy.o >/dev/null 2>&1
[root@systemhub611 snappy]# make install
make[1]: Entering directory `/opt/module/snappy'
/bin/mkdir -p '/usr/local/lib'
/bin/sh ./libtool --mode=install /usr/bin/install -c libsnappy.la '/usr/local/lib'
libtool: install: /usr/bin/install -c .libs/libsnappy.so.1.3.0 /usr/local/lib/libsnappy.so.1.3.0
libtool: install: (cd /usr/local/lib && { ln -s -f libsnappy.so.1.3.0 libsnappy.so.1 || { rm -f libsnappy.so.1 && ln -s libsnappy.so.1.3.0 libsnappy.so.1; }; })
查看snappy库文件
[root@systemhub611 snappy]# ls -lh /usr/local/lib |grep snappy
-rw-r--r--. 1 root root 462K Apr 1 16:45 libsnappy.a
-rwxr-xr-x. 1 root root 955 Apr 1 16:45 libsnappy.la
lrwxrwxrwx. 1 root root 18 Apr 1 16:45 libsnappy.so -> libsnappy.so.1.3.0
lrwxrwxrwx. 1 root root 18 Apr 1 16:45 libsnappy.so.1 -> libsnappy.so.1.3.0
-rwxr-xr-x. 1 root root 223K Apr 1 16:45 libsnappy.so.1.3.0
[root@systemhub611 snappy]#
8.1.13 编译安装 protobuf
[root@systemhub611 software]# tar -zvxf protobuf-2.5.0.tar.gz -C /opt/module/
protobuf-2.5.0/
protobuf-2.5.0/protobuf.egg-info/
protobuf-2.5.0/protobuf.egg-info/namespace_packages.txt
protobuf-2.5.0/protobuf.egg-info/top_level.txt
[root@systemhub611 protobuf]# ./configure
checking whether to enable maintainer-specific portions of Makefiles... yes
checking build system type... x86_64-unknown-linux-gnu
checking host system type... x86_64-unknown-linux-gnu
checking target system type... x86_64-unknown-linux-gnu
checking for a BSD-compatible install... /usr/bin/install -c
checking whether build environment is sane... yes
[root@systemhub611 protobuf]# make
make all-recursive
make[1]: Entering directory `/opt/module/protobuf'
Making all in .
make[2]: Entering directory `/opt/module/protobuf'
make[2]: Leaving directory `/opt/module/protobuf'
Making all in src
make[2]: Entering directory `/opt/module/protobuf/src'
[root@systemhub611 protobuf]# make install
Making install in .
make[1]: Entering directory `/opt/module/protobuf'
make[2]: Entering directory `/opt/module/protobuf'
make[2]: Nothing to be done for `install-exec-am'.
查看protobuf版本以测试是否安装成功.
[root@systemhub611 protobuf]# protoc --version
libprotoc 2.5.0
[root@systemhub611 protobuf]#
8.1.14 编译hadoop native
[root@systemhub611 software]# tar -zxvf hadoop-2.7.2-src.tar.gz
hadoop-2.7.2/
hadoop-2.7.2/libexec/
hadoop-2.7.2/libexec/yarn-config.sh
hadoop-2.7.2/libexec/hadoop-config.sh
hadoop-2.7.2/libexec/mapred-config.cmd
[root@systemhub611 native]# cp ./* /opt/module/hadoop/lib/native/
[root@systemhub611 native]# hadoop checknative
19/04/01 20:50:03 WARN bzip2.Bzip2Factory: Failed to load/initialize native-bzip2 library system-native, will use pure-Java version
19/04/01 20:50:03 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
Native library checking:
hadoop: true /opt/module/hadoop/lib/native/libhadoop.so
zlib: true /lib64/libz.so.1
snappy: true /opt/module/hadoop/lib/native/libsnappy.so.1
lz4: true revision:99
bzip2: false
openssl: true /usr/lib64/libcrypto.so
[root@systemhub611 native]#
8.2 Hadoop压缩配置
8.28.2.1 MR支持压缩编码
| 压缩格式 | 工具 | 算法 | 文件扩展名 | 是否可切分 |
| DEFAULT | 无 | DEFAULT | .default | 否 |
| Gzip | gzip | DEFAULT | .gz | 否 |
| bzip2 | bzip2 | bzip2 | .bz2 | 是 |
| LZO | lzop | LZO | .lzo | 是 |
| Snappy | 无 | Snappy | .snappy | 否 |
为了支持多种压缩/解压缩算法,Hadoop引入了编码/解码器
| 压缩格式 | 对应的编码/解码器 |
| DEFLATE | org.apache.hadoop.io.compress.DefaultCodec |
| gzip | org.apache.hadoop.io.compress.BZip2Codec |
| bzip2 | org.apache.hadoop.io.compress.BZip2Codec |
| LZO | com.hadoop.compression.lzo.LzopCodec |
| Snappy | org.apache.hadoop.io.compress.SnappyCodec |
压缩性能比较
| 压缩算法 | 原始文件大小 | 压缩文件大小 | 压缩速度 | 解压速度 |
| gzip | 8.3GB | 1.8GB | 17.5MB/s | 58MB/s |
| bzip2 | 8.3GB | 1.1GB | 2.4MB/s | 9.5MB/s |
| LZO | 8.3GB | 2.9GB | 49.3MB/s | 74.6MB/s |
| snappy | 8.3GB | 1.5GB | 250MB/s | 500MB/s |
8.2.2 压缩参数配置
要在Hadoop中启用压缩,可以配置如下参数：
| 参数 | 默认值 | 阶段 | 建议 |
| io.compression.codecs (在core-site.xml中配置) | org.apache.hadoop.io.compress.DefaultCodec, org.apache.hadoop.io.compress.GzipCodec,org.apache.hadoop.io.compress.BZip2Codec | 输入压缩 | Hadoop使用文件扩展名判断是否支持某种编解码器 |
| mapreduce.map.output.compress (在mapred-site.xml中配置) | false | mapper输出 | 这个参数设为true启用压缩 |
| mapreduce.map.output.compress.codec (在core-site.xml中配置) | field2 | field3 | field3 |
| io.compression.codecs (在mapred-site.xml中配置) | org.apache.hadoop.io.compress.DefaultCodec | mapper输出 | 使用LZO或snappy编解码器在此阶段压缩数据 |
| mapreduce.output.fileoutputformat.compress (在mapred-site.xml中配置) | false | reducer输出 | 这个参数设为true启用压缩 |
| mapreduce.output.fileoutputformat.compress.codec(在mapred-site.xml中配置) | org.apache.hadoop.io.compress. DefaultCodec | reducer输出 | 使用标准工具或者编解码器,如gzip和bzip2 |
| mapreduce.output.fileoutputformat.compress.type (在mapred-site.xml中配置) | RECORD | reducer输出 | SequenceFile输出使用的压缩类型:NONE和BLOCK |
8.3 开启Map输出阶段压缩
开启map输出阶段压缩可以减少job中map和Reduce task间数据传输量.
开启hive中间传输数据压缩功能.
hive (default)> set hive.exec.compress.intermediate=true;
hive (default)> set hive.exec.compress.intermediate;
hive.exec.compress.intermediate=true
hive (default)>
开启mapreduce中map输出压缩功能
hive (default)> set mapreduce.map.output.compress=true;
hive (default)> set mapreduce.map.output.compress;
mapreduce.map.output.compress=true
hive (default)>
设置mapreduce中map输出数据的压缩方式.
hive (default)> set mapreduce.map.output.compress.codec=org.apache.hadoop.io.compress.SnappyCodec;
hive (default)> set mapreduce.map.output.compress.codec;
mapreduce.map.output.compress.codec=org.apache.hadoop.io.compress.SnappyCodec
hive (default)>
执行查询语句
hive (default)> select count(ename) name from emp;
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.21 sec HDFS Read: 7685 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 210 msec
OK
ename
9
Time taken: 39.62 seconds, Fetched: 1 row(s)
hive (default)>
8.4 开启Reduce输出阶段压缩
当Hive将输出写入到表中时,输出内容同样可以进行压缩,属性
hive.exec.compress.output控制着这个功能,开发者可能需要保持默认设置文件中的默认值false,这样默认的输出就是非压缩的纯文本文件了,也可以通过在查询语句或执行脚本中设置这个值为true,来开启输出结果压缩功能.开启hive最终输出数据压缩功能
hive (default)> set hive.exec.compress.output=true;
hive (default)> set hive.exec.compress.output;
hive.exec.compress.output=true
hive (default)>
开启mapreduce最终输出数据压缩
hive (default)> set mapreduce.output.fileoutputformat.compress=true;
hive (default)> set mapreduce.output.fileoutputformat.compress;
mapreduce.output.fileoutputformat.compress=true
hive (default)>
设置mapreduce最终数据输出压缩方式
hive (default)> set mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.SnappyCodec;
hive (default)> set mapreduce.output.fileoutputformat.compress.codec;
mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.SnappyCodec
hive (default)>
设置mapreduce最终数据输出压缩为块压缩
hive (default)> set mapreduce.output.fileoutputformat.compress.type=BLOCK;
hive (default)> set mapreduce.output.fileoutputformat.compress.type;
mapreduce.output.fileoutputformat.compress.type=BLOCK
测试输出结果是否是压缩文件
hive (default)> insert overwrite local directory '/opt/module/datas/distribute-result' select * from emp distribute by deptno sort by empno desc;
[root@systemhub711 datas]# cat distribute-result/
cat: distribute-result/: Is a directory
[root@systemhub711 datas]# cd distribute-result/
[root@systemhub711 distribute-result]# ll
total 4
-rw-r--r--. 1 root root 424 Apr 1 22:07 000000_0.snappy
[root@systemhub711 distribute-result]# cat 000000_0.snappy
����<7939KINGSCLADDJHEW75661993-07-123000.020.0\N
7788FOES4(EDFDFD76984$4-09-1795.3 3T2JAMSKKIHNGSEHN77693$1-06-23116gR�ADAMSJUSHHWESD45521985-05-1625524.0�.�@654SOCTDMANSJUS855j86-02-142852.3��`JOSSSJDHYHDSDS45451874j(52894.252iT521WAROSSJDHHJDJX78�D84-06-121250.185�H30
7499ALLTESALES�
/k5X369SMITHCLERKSKLD790%7�2316
T0-12-17800.020.0\N
[root@systemhub711 distribute-result]#
8.5 文件存储格式
Hive支持的存储数的格式主要有 :
TEXTFILE/SEQUENCEFILE/ORC/PARQUET.
TEXTFILE和SEQUENCEFILE存储格式是基于行式存储.
ORC和PARQUET是基于列式存储.
8.5.1 列式存储 & 行式存储
图片中左边为逻辑表,右边第一个为行式存储,第二个为列式存储.
行存储的特点 : 查询满足条件的一整行数据的时候,列存储则需要去每个聚集的字段找到对应的每个列的值,行存储只需要找到其中一个值,其余的值都在相邻地方,所以此时行存储查询的速度更快.
列存储的特点 : 因为每个字段的数据聚集存储,在查询只需要少数几个字段的时候,能大大减少读取的数据量,每个字段的数据类型一定是相同的,列式存储可以针对性的设计更好的设计压缩算法.
8.5.2 TextFile格式
默认格式,数据不做压缩,磁盘开销大,数据解析开销大,可结合Gzip、Bzip2使用,但使用Gzip这种方式,hive不会对数据进行切分,从而无法对数据进行并行操作.
8.5.3 Orc格式
Orc(Optimized Row Columnar )Hive 0.11版里引入的新的存储格式.
可以看到每个Orc文件由1个或多个stripe组成,每个stripe250MB大小,这个Stripe实际相当于RowGroup概念,不过大小由4MB->250MB,这样应该能提升顺序读的吞吐率,每个Stripe里有三部分组成,分别是Index Data,Row Data,Stripe Footer:
Index Data : 一个轻量级的index,默认是每隔1W行做一个索引,这里做的索引应该只是记录某行的各字段在Row Data中的offset.
Row Data : 存的是具体的数据,先取部分行,然后对这些行按列进行存储,对每个列进行了编码,分成多个Stream来存储.Stripe Footer : 存的是各个Stream的类型,长度等信息.
每个文件有一个File Footer,这里面存的是每个Stripe的行数,每个Column的数据类型信息等,每个文件的尾部是一个PostScript,这里面记录了整个文件的压缩类型以及FileFooter的长度信息等,在读取文件时,会seek到文件尾部读PostScript,从里面解析到File Footer长度,再读FileFooter,从里面解析到各个Stripe信息,再读各个Stripe,即从后往前读.
8.5.4 Parquet格式
Parquet文件的格式如下图所示
Parquet是面向分析型业务的列式存储格式,由Twitter和Cloudera合作开发,2015年5月从Apache的孵化器里毕业成为Apache顶级项目.
Parquet文件是以二进制方式存储的,所以是不可以直接读取的,文件中包括该文件的数据和元数据,因此Parquet格式文件是自解析.
通常情况下,在存储Parquet数据的时候会按照Block大小设置行组的大小,由于一般情况下每一个Mapper任务处理数据的最小单位是一个Block,这样可以把每一个行组由一个Mapper任务处理,增大任务执行并行度.
上图展示了一个Parquet文件的内容,一个文件中可以存储多个行组,文件的首位都是该文件的Magic Code,用于校验它是否是一个Parquet文件,Footer length记录了文件元数据的大小,通过该值和文件长度可以计算出元数据的偏移量,文件的元数据中包括每一个行组的元数据信息和该文件存储数据的Schema信息,除了文件中每一个行组的元数据,每一页的开始都会存储该页的元数据,在Parquet中,有三种类型的页：数据页、字典页和索引页,数据页用于存储当前行组中该列的值,字典页存储该列值的编码字典,每一个列块中最多包含一个字典页,索引页用来存储当前行组下该列的索引,目前Parquet中还不支持索引页.
8.6 存储和压缩结合
8.6.1 修改Hadoop集群具有Snappy压缩方式
1.查看hadoop checknative命令使用.
[root@systemhub511 native]# hadoop
Usage: hadoop [--config confdir] [COMMAND | CLASSNAME]
checknative [-a|-h] check native hadoop and compression libraries
Most commands print help when invoked w/o parameters.
[root@systemhub511 native]#
2.查看hadoop支持的压缩方式.
[root@systemhub511 native]# hadoop checknative
19/04/01 23:14:06 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Native library checking:
hadoop: false
zlib: false
snappy: false
lz4: false
bzip2: false
openssl: false
19/04/01 23:14:07 INFO util.ExitUtil: Exiting with status 1
[root@systemhub511 native]#
3.将编译好支持Snappy压缩hadoop-2.7.2.tar.gz包导入到/opt/software中.
4.将hadoop-2.7.2.tar.gz解压到当前路径.
[root@systemhub611 software]# tar -zxvf hadoop-2.7.2.-src.tar.gz
hadoop-2.7.2/
hadoop-2.7.2/libexec/
hadoop-2.7.2/libexec/yarn-config.sh
hadoop-2.7.2/libexec/hadoop-config.sh
hadoop-2.7.2/libexec/mapred-config.cmd
5.进入到/opt/software/hadoop-2.7.2/lib/native路径可以看到支持Snappy压缩动态链接库.
[root@systemhub611 opt]# cd software/
[root@systemhub611 software]# cd hadoop-2.7.2/
[root@systemhub611 hadoop-2.7.2]# cd lib/native/
[root@systemhub611 native]# pwd
/opt/software/hadoop-2.7.2/lib/native
[root@systemhub611 native]# ll
total 5188
-rw-r--r--. 1 root root 1210260 Sep 1 2017 libhadoop.a
-rw-r--r--. 1 root root 1487268 Sep 1 2017 libhadooppipes.a
lrwxrwxrwx. 1 root root 18 Apr 1 19:30 libhadoop.so -> libhadoop.so.1.0.0
-rwxr-xr-x. 1 root root 716316 Sep 1 2017 libhadoop.so.1.0.0
-rw-r--r--. 1 root root 582048 Sep 1 2017 libhadooputils.a
-rw-r--r--. 1 root root 364860 Sep 1 2017 libhdfs.a
lrwxrwxrwx. 1 root root 16 Apr 1 19:30 libhdfs.so -> libhdfs.so.0.0.0
-rwxr-xr-x. 1 root root 229113 Sep 1 2017 libhdfs.so.0.0.0
-rw-r--r--. 1 root root 472950 Sep 1 2017 libsnappy.a
-rwxr-xr-x. 1 root root 955 Sep 1 2017 libsnappy.la
lrwxrwxrwx. 1 root root 18 Apr 1 19:30 libsnappy.so -> libsnappy.so.1.3.0
lrwxrwxrwx. 1 root root 18 Apr 1 19:30 libsnappy.so.1 -> libsnappy.so.1.3.0
-rwxr-xr-x. 1 root root 228177 Sep 1 2017 libsnappy.so.1.3.0
[root@systemhub611 native]#
6.将/opt/software/hadoop-2.7.2/lib/native目录下所有内容拷贝到开发集群 的/opt/module/hadoop-2.7.2/lib/native路径中.
[root@systemhub611 native]# cp ./* /opt/module/hadoop/lib/native/
7.分发集群
[root@systemhub611 lib]# rsync -rvl /opt/module/hadoop/lib/native/ root@systemhub511:/opt/module/hadoop/lib/native/
sending incremental file list
libhadoop.a
libhadoop.so
libhadoop.so.1.0.0
libhadooppipes.a
libhadooputils.a
libhdfs.a
libhdfs.so
libhdfs.so.0.0.0
libsnappy.a
libsnappy.la
libsnappy.so
libsnappy.so.1
libsnappy.so.1.3.0
sent 6617750 bytes received 30829 bytes 4432386.00 bytes/sec
total size is 6693730 speedup is 1.01
[root@systemhub611 lib]#
[root@systemhub611 lib]# rsync -rvl /opt/module/hadoop/lib/native/ root@systemhub711:/opt/module/hadoop/lib/native/
sending incremental file list
libhadoop.a
libhadoop.so
libhadoop.so.1.0.0
libhadooppipes.a
libhadooputils.a
libhdfs.a
libhdfs.so
libhdfs.so.0.0.0
libsnappy.a
libsnappy.la
libsnappy.so
libsnappy.so.1
libsnappy.so.1.3.0
sent 6613502 bytes received 38665 bytes 4434778.00 bytes/sec
total size is 6693730 speedup is 1.01
[root@systemhub611 lib]#
8.再次查看hadoop集群支持压缩类型.
systemhub511
[root@systemhub511 hadoop]# hadoop checknative
19/04/01 23:24:45 WARN bzip2.Bzip2Factory: Failed to load/initialize native-bzip2 library system-native, will use pure-Java version
19/04/01 23:24:45 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
Native library checking:
hadoop: true /opt/module/hadoop/lib/native/libhadoop.so
zlib: true /lib64/libz.so.1
snappy: true /opt/module/hadoop/lib/native/libsnappy.so.1
lz4: true revision:99
bzip2: false
openssl: false Cannot load libcrypto.so (libcrypto.so: cannot open shared object file: No such file or directory)!
[root@systemhub511 hadoop]#
systemhub611
[root@systemhub611 lib]# hadoop checknative
19/04/01 23:25:02 WARN bzip2.Bzip2Factory: Failed to load/initialize native-bzip2 library system-native, will use pure-Java version
19/04/01 23:25:02 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
Native library checking:
hadoop: true /opt/module/hadoop/lib/native/libhadoop.so
zlib: true /lib64/libz.so.1
snappy: true /opt/module/hadoop/lib/native/libsnappy.so.1
lz4: true revision:99
bzip2: false
openssl: true /usr/lib64/libcrypto.so
[root@systemhub611 lib]#
systemhub711
[root@systemhub711 hive]# hadoop checknative
19/04/01 23:25:28 WARN bzip2.Bzip2Factory: Failed to load/initialize native-bzip2 library system-native, will use pure-Java version
19/04/01 23:25:28 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
Native library checking:
hadoop: true /opt/module/hadoop/lib/native/libhadoop.so
zlib: true /lib64/libz.so.1
snappy: true /opt/module/hadoop/lib/native/libsnappy.so.1
lz4: true revision:99
bzip2: false
openssl: false Cannot load libcrypto.so (libcrypto.so: cannot open shared object file: No such file or directory)!
[root@systemhub711 hive]#
9.重新启动hadoop集群和hive.
8.6.2 测试存储 & 压缩
ORC存储方式压缩
| Key | Default | Notes |
| orc.compress | ZLIB | high level compression (one of NONE,ZLIB, SNAPPY) |
| orc.compress.size | 262,144 | number of bytes in each compression chunk |
| orc.stripe.size | 67,108,864 | number of bytes in each stripe |
| orc.row.index.stride | 10,000 | number of rows between index entries(must be >= 1000) |
| orc.create.index | true | whether to create row indexes |
| orc.bloom.filter.columns | “” | comma separated list of column names for which bloom filter should be created |
| orc.bloom.filter.fpp | 0.05 | false positive probability for bloom filter(must >0.0 and <1.0) |
9. 企业级调优
9.1 Fetch抓取
Fetch抓取是指:Hive中对某些情况的查询可以不必使用MapReduce计算.
例如 :
SELECT * FROM employees;在这种情况下,Hive可以简单地读取employee对应的存储目录下的文件,然后输出查询结果到控制台.在hive-default.xml.template文件中
hive.fetch.task.conversion默认是more,老版本hive默认是minimal,该属性修改为more以后,在全局查找、字段查找、limit查找等都不会执行MapReduce.
<property>
<name>hive.fetch.task.conversion</name>
<value>more</value>
<description>
Expects one of [none, minimal, more].
Some select queries can be converted to single FETCH task minimizing latency.
Currently the query should be single sourced not having any subquery and should not have
any aggregations or distincts (which incurs RS), lateral views and joins.
0. none : disable hive.fetch.task.conversion
1. minimal : SELECT STAR, FILTER on partition columns, LIMIT only
2. more : SELECT, FILTER, LIMIT only (support TABLESAMPLE and virtual columns)
</description>
</property>
1.将hive.fetch.task.conversion属性设置成none,然后执行查询语句,都会执行MapReduce程序.
当前默认属性是more.
hive (default)> set hive.fetch.task.conversion;
hive.fetch.task.conversion=more
hive (default)>
设置属性为none.
hive (default)> set hive.fetch.task.conversion=none;
hive (default)> set hive.fetch.task.conversion;
hive.fetch.task.conversion=none
hive (default)>
查询数据表,结果每次查询都会执行MapReduce程序.
hive (default)> select * from emp;
Query ID = root_20190401234234_12968ad3-7c1c-4e6b-a06c-7e06deabe984
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Cumulative CPU: 1.55 sec HDFS Read: 4192 HDFS Write: 472 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 550 msec
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 35.844 seconds, Fetched: 9 row(s)
hive (default)>
2.将hive.fetch.task.conversion属性设置成more,然后执行查询语句,如下查询方式都不会执行MapReduce程序.
hive (default)> set hive.fetch.task.conversion=more;
hive (default)> select * from emp;
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
Time taken: 0.134 seconds, Fetched: 9 row(s)
hive (default)>
9.2 本地模式
大多数Hadoop Job是需要Hadoop提供的完整的可扩展性来处理大数据集.
不过,有时Hive的输入数据量是非常小的,在这种情况下,为查询触发执行任务时消耗可能会比实际job的执行时间要多的多,对于大多数这种情况,Hive可以通过本地模式在单台机器上处理所有的任务,对于小数据集,执行时间可以明显被缩短.
开发者可以通过设置
hive.exec.mode.local.auto的值为true,来让Hive在适当的时候自动启动这个优化.
// 开启本地 MapReduce
hive (default)> set hive.exec.mode.local.auto=true;
// 设置LocalMapReduce的最大输入数据量,当输入数据量小于这个值时采用LocalMapReduce方式,默认为134217728,即128M.
hive (default)> set hive.exec.mode.local.auto.inputbytes.max=50000000;
// 设置LocalMapReduce最大输入文件个数,当输入文件个数小于这个值时采用LocalMapReduce方式,默认为4.
hive (default)> set hive.exec.mode.local.auto.input.files.max=10;
开启本地模式,执行查询语句,并查看计算运行时间,所用查询时间为2.716秒
hive (default)> set hive.exec.mode.local.auto=true;
hive (default)> select * from emp cluster by deptno;
Automatically selecting local only mode for query
Query ID = root_20190401235135_caee5452-243d-4299-b7a2-37ecf31c53c4
MapReduce Jobs Launched:
Stage-Stage-1: HDFS Read: 2724 HDFS Write: 41613642 SUCCESS
Total MapReduce CPU Time Spent: 0 msec
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
Time taken: 2.716 seconds, Fetched: 9 row(s)
hive (default)>
关闭本地模式,执行查询语句,并查看计算运行时间,所用查询时间为35.145秒.
hive (default)> set hive.exec.mode.local.auto=flase;
hive (default)> select * from emp cluster by deptno;
Query ID = root_20190401235235_043bf057-0655-46f1-8003-0c0a842009ca
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 4.31 sec HDFS Read: 8480 HDFS Write: 472 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 310 msec
OK
emp.empno emp.ename emp.job emp.mgr emp.hiredate emp.sal emp.comm emp.deptno
7939 KINGS CLADDJHEW 7566 1993-07-12 3000.0 20.0 NULL
7788 FOESS CLAEDFDFD 7698 1994-09-17 950.0 30.0 NULL
7782 JAMSK KIHNGSEHN 7769 1991-06-23 1100.0 20.0 NULL
7698 ADAMS JUSHHWESD 4552 1985-05-16 25524.02 30.0 NULL
7654 SOCTD MANSJUSSD 4855 1996-02-14 2852.3 30.0 NULL
7566 JOSSS JDHYHDSDS 4545 1874-05-15 2894.25 20.0 NULL
7369 SMITH CLERKSKLD 7902 1980-12-17 800.0 20.0 NULL
7521 WAROS SJDHHJDJX 7869 1984-06-12 1250.18 500.0 30
7499 ALLTE SALESMANS 7689 1987-02-23 1600.0 300.0 30
Time taken: 35.145 seconds, Fetched: 9 row(s)
hive (default)>
9.3 数据表优化
9.3.1 小表/大表Join
将key相对分散,并且数据量小的表放在join的左边,这样可以有效减少内存溢出错误发生的几率,再进一步,可以使用Group让小的维度表(1000条以下的记录条数)进内存,在map端完成reduce.
实际测试发现 : 新版本hive已经对小表JOIN大表和大表JOIN小表进行了优化,小表放在左边和右边已经没有明显区别.
9.3.2 大表Join大表
1.空KEY过滤
有时join超时是因为某些key对应的数据太多,而相同key对应的数据都会发送到相同的reducer上,从而导致内存不够,此时应该仔细分析这些异常的key,很多情况下,这些key对应的数据是异常数据,需要在SQL语句中进行过滤,例如key对应的字段为空.
9.3.3 MapJoin
如果不指定MapJoin或者不符合MapJoin的条件,那么Hive解析器会将Join操作转换成Common Join,即在Reduce阶段完成join,容易发生数据倾斜,可以用MapJoin把小表全部加载到内存在map端进行join,避免reducer处理.
9.3.4 Group By
默认情况下,Map阶段同一Key数据分发给一个reduce,当一个key数据过大时就倾斜了.
并不是所有的聚合操作都需要在Reduce端完成,很多聚合操作都可以先在Map端进行部分聚合,最后在Reduce端得出最终结果.
9.3.5 Count(Distinct) 去重统计
数据量小的时候无所谓,数据量大的情况下,由于COUNT DISTINCT操作需要用一个Reduce Task来完成,这一个Reduce需要处理的数据量太大,就会导致整个Job很难完成,一般COUNT DISTINCT使用先GROUP BY再COUNT的方式替换.
9.3.6 笛卡尔积
尽量避免笛卡尔积,join的时候不加on条件,或者无效的on条件,Hive只能使用1个reducer来完成笛卡尔积.
9.3.7 行列过滤
列处理 : 在SELECT中,只拿需要的列,如果有,尽量使用分区过滤,少用SELECT *
行处理 : 在分区剪裁中,当使用外关联时,如果将副表的过滤条件写在Where后面,那么就会先全表关联,之后再过滤.
9.3.8 动态分区调整
关系型数据库中,对分区表Insert数据时候,数据库自动会根据分区字段的值,将数据插入到相应的分区中,Hive中也提供了类似的机制,即动态分区(Dynamic Partition),只不过,使用Hive的动态分区,需要进行相应的配置.
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9.4 数据倾斜
9.4.1 合理设置Map数
9.4.2 小文件进行合并
9.4.3 复杂文件增加Map数
9.4.4 合理设置Reduce数
9.5 并行执行
9.6 严格模式
9.7 JVM重用
9.8 推测执行
9.10 执行计划 (Explain)
10. 修仙之道 技术架构迭代 登峰造极之势
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